Maneuvering a general 2-trailer with a car-like tractor in backward motion is a task that requires a significant skill to master and is unarguably one of the most complicated tasks a truck driver has to perform. This paper presents a path planning and path-following control solution that can be used to automatically plan and execute difficult parking and obstacle avoidance maneuvers by combining backward and forward motion. A lattice-based path planning framework is developed in order to generate kinematically feasible and collision-free paths and a path-following controller is designed to stabilize the lateral and angular path-following error states during path execution. To estimate the vehicle state needed for control, a nonlinear observer is developed, which only utilizes information from sensors that are mounted on the car-like tractor, making the system independent of additional trailer sensors. The proposed path-planning and path-following control framework is implemented on a full-scale test vehicle and results from simulations and real-world experiments are presented.

INTRODUCTION: The HeartMate 3^™ has shown lower rates of adverse events compared to previous devices due to the design and absence of mechanical bearings. For previous devices, sound analysis emerged as a way to assess pump function. The aims of this study were to determine if sound analysis can be applied to the HeartMate 3 in vivo and in vitro and to evaluate an electronic stethoscope.

METHOD: Sound recordings were performed with microphones and clinical accessible electronic stethoscope. The recordings were studied in both the time and the frequency domains. Recordings from four patients were performed to determine if in vivo and in vitro recordings are comparable.

RESULTS: The results show that it is possible to detect sound from HeartMate 3 and the sound spectrum is clear. Pump frequency and frequency of the pulsatile mode are easily determined. Frequency spectra from in vitro and in vivo recordings have the same pattern, and the major proportion (96.7%) of signal power is located at the pump speed frequency ±40 Hz. The recordings from the patients show low inter-individual differences except from location of peaks originating from pump speed and harmonics. Electronic stethoscopes could be used for sound recordings, but the dedicated equipment showed a clearer sound spectrum.

DISCUSSION: The results show that acoustic analysis can also be performed with the HeartMate 3 and that in vivo and in vitro sound spectrum is similar. The frequency spectra are different from previous devices, and methods for assessing pump function or thrombosis need further evaluation.

Objectives: The aim of this study was to compare listening effort, as estimated via pupillary response, during a speech-in-noise test in bone-anchored hearing system (BAHS) users wearing three different sound processors. The three processors, Ponto Pro (PP), Ponto 3 (P3), and Ponto 3 SuperPower (P3SP), differ in terms of maximum force output (MFO) and MFO algorithm. The hypothesis was that listeners would allocate lower listening effort with the P3SP than with the PP, as a consequence of a higher MFO and, hence, fewer saturation artifacts in the signal. Design: Pupil dilations were recorded in 21 BAHS users with a conductive or mixed hearing loss, during a speech-in-noise test performed at positive signal-to-noise ratios (SNRs), where the speech and noise levels were individually adjusted to lead to 95% correct intelligibility with the PP. The listeners had to listen to a sentence in noise, retain it for 3 seconds and then repeat it, while an eye-tracking camera recorded their pupil dilation. The three sound processors were tested in random order with a single-blinded experimental design. Two conditions were performed at the same SNR: Condition 1, where the speech level was designed to saturate the PP but not the P3SP, and condition 2, where the overall sound level was decreased relative to condition 1 to reduce saturation artifacts. Results: The P3SP led to higher speech intelligibility than the PP in both conditions, while the performance with the P3 did not differ from the performance with the PP and the P3SP. Pupil dilations were analyzed in terms of both peak pupil dilation (PPD) and overall pupil dilation via growth curve analysis (GCA). In condition 1, a significantly lower PPD, indicating a decrease in listening effort, was obtained with the P3SP relative to the PP. The PPD obtained with the P3 did not differ from the PPD obtained with the other two sound processors. In condition 2, no difference in PPD was observed across the three processors. The GCA revealed that the overall pupil dilation was significantly lower, in both conditions, with both the P3SP and the P3 relative to the PP, and, in condition 1, also with the P3SP relative to the P3. Conclusions: The overall effort to process a moderate to loud speech signal was significantly reduced by using a sound processor with a higher MFO (P3SP and P3), as a consequence of fewer saturation artifacts. These findings suggest that sound processors with a higher MFO may help BAHS users in their everyday listening scenarios, in particular in noisy environments, by improving sound quality and, thus, decreasing the amount of cognitive resources utilized to process incoming speech sounds.

Modeling and failure prediction are important tasks in many engineering systems. For these tasks, the machine learning literature presents a large variety of models such as classification trees, random forest, artificial neural networks, among others. Standard statistical models such as the logistic regression, linear discriminant analysis, k-nearest neighbors, among others, can be applied. This work evaluates advantages and limitations of statistical and machine learning methods to predict failures in industrial robots. The work is based on data from more than five thousand robots in industrial use. Furthermore, a new approach combining standard statistical and machine learning models, named hybrid gradient boosting, is proposed. Results show that the hybrid gradient boosting achieves significant improvement as compared to statistical and machine learning methods. Furthermore, local joint information has been identified as the main driver for failure detection, whereas failure classification can be improved using additional information from different joints and hybrid models. (C) 2019 Elsevier Ltd. All rights reserved.

Differential graphical games have been introduced in the literature to solve state synchronization problem for linear homogeneous agents. When the agents are heterogeneous, the previous notion of graphical games cannot be used anymore and a new definition is required. In this paper, we define a novel concept of differential graphical games for linear heterogeneous agents subject to external unmodeled disturbances, which contain the previously introduced graphical game for homogeneous agents as a special case. Using our new formulation, we can solve both the output regulation and H-infinity output regulation problems. Our graphical game framework yields coupled Hamilton-Jacobi-Bellman equations, which are, in general, impossible to solve analytically. Therefore, we propose a new actor-critic algorithm to solve these coupled equations numerically in real time. Moreover, we find an explicit upper bound for the overall L2-gain of the output synchronization error with respect to disturbance. We demonstrate our developments by a simulation example.

A signed network is a network in which each link is associated with a positive or negative sign. Models for nodes interacting over such signed networks arise from various biological, social, political, and economic systems. As modifications to the conventional DeGroot dynamics for positive links, two basic types of negative interactions along negative links, namely, the opposing rule and the repelling rule, have been proposed and studied in the literature. This paper reviews a few fundamental convergence results for such dynamics over deterministic or random signed networks under a unified algebraic-graphical method. We show that a systematic tool for studying node state evolution over signed networks can be obtained utilizing generalized Perron-Frobenius theory, graph theory, and elementary algebraic recursions.

We consider a linear state estimation problem, where, in addition to the usual timestamped measurements, observations with uncertain timestamps are available. Such observations could, e.g., come from traces left by a target in a tracking scenario or from witnesses of an event and have the potential to improve the estimation accuracy significantly. We derive the posterior distribution and point estimators for a linear Gaussian smoothing formulation of this problem and illustrate with two numerical examples.

This tutorial provides a gentle introduction to the particle Metropolis-Hastings (PMH) algorithm for parameter inference in nonlinear state-space models together with a software implementation in the statistical programming language R. We employ a step-by-step approach to develop an implementation of the PMH algorithm (and the particle filter within) together with the reader. This final implementation is also available as the package pmhtutorial from the Comprehensive R Archive Network (CRAN) repository. Throughout the tutorial, we provide some intuition as to how the algorithm operates and discuss some solutions to problems that might occur in practice. To illustrate the use of PMH, we consider parameter inference in a linear Gaussian state-space model with synthetic data and a nonlinear stochastic volatility model with real-world data.

Migraine is a neurological disorder characterized by persisting attacks, underlined by the sensitivity to light. One of the leading reasons that make migraine a bigger issue is that it cannot be diagnosed easily by physicians because of the numerous overlapping symptoms with other diseases, such as epilepsy and tension-headache. Consequently, studies have been growing on how to make a computerized decision support system for diagnosis of migraine. In most laboratory studies, flash stimulation is used during the recording of electroencephalogram (EEG) signals with different frequencies and variable (seconds) time windows. The main contribution of this study is the investigation of the effects of flash stimulation on the classification accuracy, and how to find the effective window length for EEG signal classification. To achieve this, we tested different machine learning algorithms on the EEG signals features extracted by using discrete wavelet transform. Our tests on the real-world dataset, recorded in the laboratory, show that the flash stimulation can improve the classification accuracy for more than 10%. Not surprisingly, it is seen that the same holds for the selection of time window length, i.e. the selection of the proper window length is crucial for the accurate migraine identification. (C) 2018 Elsevier Ltd. All rights reserved.

This paper proposes a new method to identify nonlinear state-space systems from heterogeneous datasets. The method is described in the context of identifying biochemical/gene networks (i.e., identifying both reaction dynamics and kinetic parameters) from experimental data. Simultaneous integration of various datasets has the potential to yield better performance for system identification. Data collected experimentally typically vary depending on the specific experimental setup and conditions. Typically, heterogeneous data are obtained experimentally through 1) replicate measurements from the same biological system or 2) application of different experimental conditions such as changes/perturbations in biological inductions, temperature, gene knock-out, gene over-expression, etc. We formulate here the identification problem using a Bayesian learning framework that makes use of “sparse group” priors to allow inference of the sparsest model that can explain the whole set of observed heterogeneous data. To enable scale up to large number of features, the resulting nonconvex optimization problem is relaxed to a reweighted Group Lasso problem using a convex-concave procedure. As an illustrative example of the effectiveness of our method, we use it to identify a genetic oscillator (generalized eight species repressilator). Through this example we show that our algorithm outperforms Group Lasso when the number of experiments is increased, even when each single time-series dataset is short. We additionally assess the robustness of our algorithm against noise by varying the intensity of process noise and measurement noise.

The behavior of a person during a conversation typically involves both auditory and visual attention. Visual attention implies that the person directs his or her eye gaze toward the sound target of interest, and hence, detection of the gaze may provide a steering signal for future hearing aids. The steering could utilize a beamformer or the selection of a specific audio stream from a set of remote microphones. Previous studies have shown that eye gaze can be measured through electrooculography (EOG). To explore the precision and real-time feasibility of the methodology, seven hearing-impaired persons were tested, seated with their head fixed in front of three targets positioned at -30 degrees, 0 degrees, and +30 degrees azimuth. Each target presented speech from the Danish DAT material, which was available for direct input to the hearing aid using head-related transfer functions. Speech intelligibility was measured in three conditions: a reference condition without any steering, a condition where eye gaze was estimated from EOG measures to select the desired audio stream, and an ideal condition with steering based on an eye-tracking camera. The "EOG-steering" improved the sentence correct score compared with the "no-steering" condition, although the performance was still significantly lower than the ideal condition with the eye-tracking camera. In conclusion, eye-gaze steering increases speech intelligibility, although real-time EOG-steering still requires improvements of the signal processing before it is feasible for implementation in a hearing aid.

The stable spline (SS) kernel and the diagonal correlated (DC) kernel are two kernels that have been applied and studied extensively for kernel-based regularized LTI system identification. In this note, we show that similar to the derivation of the SS kernel, the continuous-time DC kernel can be derived by applying the same "stable" coordinate change to a "generalized" first-order spline kernel, and thus, can be interpreted as a stable generalized first-order spline kernel. This interpretation provides new facets to understand the properties of the DC kernel. In particular, we derive a new orthonormal basis expansion of the DC kernel and the explicit expression of the norm of the reproducing kernel Hilbert space associated with the DC kernel. Moreover, for the nonuniformly sampled DC kernel, we derive its maximum entropy property and show that its kernel matrix has tridiagonal inverse.

Filtering and smoothing algorithms for linear discrete-time state-space models with skew-t-distributed measurement noise are proposed. The algorithms use a variational Bayes based posterior approximation with coupled location and skewness variables to reduce the error caused by the variational approximation. Although the variational update is done suboptimally using an expectation propagation algorithm, our simulations show that the proposed method gives a more accurate approximation of the posterior covariance matrix than an earlier proposed variational algorithm. Consequently, the novel filter and smoother outperform the earlier proposed robust filter and smoother and other existing low-complexity alternatives in accuracy and speed. We present both simulations and tests based on real-world navigation data, in particular the global positioning system data in an urban area, to demonstrate the performance of the novel methods. Moreover, the extension of the proposed algorithms to cover the case where the distribution of the measurement noise is multivariate skew-t is outlined. Finally, this paper presents a study of theoretical performance bounds for the proposed algorithms.

The models of collective decision-making considered in this paper are nonlinear interconnected cooperative systems with saturating interactions. These systems encode the possible outcomes of a decision process into different steady states of the dynamics. In particular, they are characterized by two main attractors in the positive and negative orthant, representing two choices of agreement among the agents, associated to the Perron-Frobenius eigenvector of the system. In this paper we give conditions for the appearance of other equilibria of mixed sign. The conditions are inspired by Perron-Frobenius theory and are related to the algebraic connectivity of the network. We also show how all these equilibria must be contained in a solid disk of radius given by the norm of the equilibrium point which is located in the positive orthant.

The problem of path planning for mobilesensors with the task of target monitoring is considered. A receding horizon optimal control approach based on the information filter is presented, where the limited field of view of the sensor can be modeled by introducing binary variables. The resulting nonlinear mixed integer problem to be solved in each sample, with no apparent tractable solution, is shown to be equivalent to a problem that robustly can be solved to global optimality using off-the-shelf optimization tools.

The kernel-based regularization method has two core issues: kernel design and hyperparameter estimation. In this paper, we focus on the second issue and study the properties of several hyperparameter estimators including the empirical Bayes (EB) estimator, two Steins unbiased risk estimators (SURE) (one related to impulse response reconstruction and the other related to output prediction) and their corresponding Oracle counterparts, with an emphasis on the asymptotic properties of these hyperparameter estimators. To this goal, we first derive and then rewrite the first order optimality conditions of these hyperparameter estimators, leading to several insights on these hyperparameter estimators. Then we show that as the number of data goes to infinity, the two SUREs converge to the best hyperparameter minimizing the corresponding mean square error, respectively, while the more widely used EB estimator converges to another best hyperparameter minimizing the expectation of the EB estimation criterion. This indicates that the two SUREs are asymptotically optimal in the corresponding MSE senses but the EB estimator is not. Surprisingly, the convergence rate of two SUREs is slower than that of the EB estimator, and moreover, unlike the two SUREs, the EB estimator is independent of the convergence rate of Phi(T)Phi/N to its limit, where Phi is the regression matrix and N is the number of data. A Monte Carlo simulation is provided to demonstrate the theoretical results. (C) 2018 Elsevier Ltd. All rights reserved.

There are two primary sources of sensor measurements for driver behavior profiling within insurance telematics and fleet management. The first is the on-board diagnostics system, typically found within most modern cars. The second is the global navigation satellite system, whose associated receivers commonly are embedded into smartphones or off-the-shelf telematics devices. In this paper, we present maximum likelihood and maximum a posteriori estimators for the problem of fusing speed measurements from these two sources to jointly estimate a vehicles speed and the scale factor of the wheel speed sensors. In addition, we analyze the performance of the estimators by use of the Cramer-Rao bound, and discuss the estimation of model parameters describing measurement errors and vehicle dynamics. Last, simulations and real-world data are used to show that the proposed estimators yield a substantial performance gain compared to when employing only one of the two measurement sources.

Introduction: The use of left ventricular assist device (LVAD) has grown rapidly. Adverse events do continue to occur. In recent years, analysis of LVAD sound recordings emerged as a means to monitor pump function and detect pump thrombosis. The aim of this study was to characterize the sounds from HeartMate II and to evaluate the use of handheld iOS devices for sound recordings. Method: Signal analysis of LVAD sound recordings, with dedicated recording equipment and iOS devices, was performed. Two LVADs running in mock loop circuits were compared to an implanted LVAD. Spectral analysis and parametric signal models were explored to quantify the sound and potentially detect changes in it. Results: The sound recordings of two LVADs in individual mock loop circuits and a third one implanted in a patient appeared to be similar. Qualitatively, sound characteristics were preserved following changes in pump speed. Recordings using dedicated equipment showed that HeartMate II sound comprises low-frequency components corresponding to pump impeller rotation, as well as high-frequency components due to a pulse width modulation of the electric power to the pump. These different signal components interact and result in a complicated frequency spectrum. The iPhone and iPod recordings could not reproduce the sounds as well as the dedicated equipment. In particular, lower frequencies were affected by outside disturbances. Discussion: This article outlines a systematic approach to LVAD sound analysis using signal processing methods to quantify and potentially detect changes, and describes some of the challenges, for example, with the use of inexpensive recording devices.

This note considers the identification of large-scale one-dimensional networks consisting of identical LTI dynamical systems. A subspace identification method is developed that only uses local input-output information and does not rely on knowledge about the local state interaction. The proposed identification method estimates the Markov parameters of a locally lifted system, following the state-space realization of a single subsystem. The Markov-parameter estimation is formulated as a rank minimization problem by exploiting the low-rank property and the two-layer Toeplitz structural property in the data equation, whereas the state-space realization of a single subsystem is formulated as a structured low-rank matrix-factorization problem. The effectiveness of the proposed identification method is demonstrated by simulation examples.

Time-correlated single-photon counting lidar provides very high-resolution range measurements, making the technology interesting for 3D imaging of objects behind foliage or other obscuration. We study six peak detection approaches and compare their performance from several perspectives: detection of double surfaces within the instantaneous field of view, range accuracy, performance under sparse sampling, and the number of outliers. The results presented are based on reference measurements of a characterization target. Special consideration is given to the possibility of resolving two surfaces closely separated in range within the field of view of a single pixel. An approach based on fitting a linear combination of impulse response functions to the collected data showed the best overall performance. (c) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE).

This paper presents a new method to compute upper and lower bounds of any voltage or current of an arbitrary linear electric circuit model with uncertain parameters. The bounds are in the frequency domain, and when compared to a previously proposed method, this novel approach provides a higher level of guarantee. The reason is that the bounds are not only computed for a set of fixed frequencies but also computed to a set of intervals of frequencies. The details of the proposed approach, especially the equivalent uncertain element models, are given. Additionally, tests are performed on problems with low and high number of uncertain parameters. Contrary to the classical method of Monte Carlo, the results are not based on a random choice of parameters and do not depend on the number of iterations. It is shown on an example that the classical method of Monte Carlo needs a high number of iterations to reach results in agreement with the proposed method. Then, it leads to higher computation times of several orders of magnitude.

Nonlinear cooperative systems associated to vector fields that are concave or subhomogeneous describe well interconnected dynamics that are of key interest for communication, biological, economical, and neural network applications. For this class of positive systems, we provide conditions that guarantee existence, uniqueness and stability of strictly positive equilibria. These conditions can be formulated directly in terms of the spectral radius of the Jacobian of the system. If control inputs are available, then it is shown how to use state feedback to stabilize an equilibrium point in the interior of the positive orthant.

This study proposes a new model which is fully specified for automated seizure onset detection and seizure onset prediction based on electroencephalography (EEG) measurements. We processed two archetypal EEG databases, Freiburg (intracranial EEG) and CHB-MIT (scalp EEG), to find if our model could outperform the state-of-the art models. Four key components define our model: (1) multiscale principal component analysis for EEG de-noising, (2) EEG signal decomposition using either empirical mode decomposition, discrete wavelet transform or wavelet packet decomposition, (3) statistical measures to extract relevant features, (4) machine learning algorithms. Our model achieved overall accuracy of 100% in ictal vs. inter-ictal EEG for both databases. In seizure onset prediction, it could discriminate between inter-ictal, pre-ictal, and ictal EEG with the accuracy of 99.77%, and between inter-ictal and pre-ictal EEG states with the accuracy of 99.70%. The proposed model is general and should prove applicable to other classification tasks including detection and prediction regarding bio-signals such as EMG and ECG. (C) 2017 Elsevier Ltd. All rights reserved.

In a continuous-time nonlinear driftless control system, an involutive flow is a composition of input profiles that does not excite any Lie bracket. Such flow composition is trivial, as it corresponds to a "forth and back" cyclic motion obtained rewinding the system along the same path. The aim of this paper is to show that, on the contrary, when a (nonexact) discretization of the nonlinear driftless control system is steered along the same trivial input path, it produces a net motion, which is related to the gap between the discretization used and the exact discretization given by a Taylor expansion. These violations of involutivity can be used to provide an estimate of the local truncation error of numerical integration schemes. In the special case in which the state of the driftless control system admits a splitting into shape and phase variables, our result corresponds to saying that the geometric phases of the discretization need not obey an area rule, i.e., even zero-area cycles in shape space can lead to nontrivial geometric phases. (C) 2017 Elsevier B.V. All rights reserved.

The local approach to linear parameter varying (LPV) system identification consists in interpolating individually estimated local linear time invariant (LTI) models corresponding to fixed values of the scheduling variable. It is shown in this paper that, without any global structural assumption of the considered LPV system, individually estimated local state-space LTI models do not contain sufficient information for determining similarity transformations making them coherent. It is possible to estimate these similarity transformations from input-output data under appropriate excitation conditions. (C) 2017 Published by Elsevier Ltd.

The ensemble Kalman filter (EnKF) is a Monte Carlo-based implementation of the Kalman filter (KF) for extremely high-dimensional, possibly nonlinear, and non-Gaussian state estimation problems. Its ability to handle state dimensions in the order of millions has made the EnKF a popular algorithm in different geoscientific disciplines. Despite a similarly vital need for scalable algorithms in signal processing, e.g., to make sense of the ever increasing amount of sensor data, the EnKF is hardly discussed in our field. This self-contained review is aimed at signal processing researchers and provides all the knowledge to get started with the EnKF. The algorithm is derived in a KF framework, without the often encountered geoscientific terminology. Algorithmic challenges and required extensions of the EnKF are provided, as well as relations to sigma point KF and particle filters. The relevant EnKF literature is summarized in an extensive survey and unique simulation examples, including popular benchmark problems, complement the theory with practical insights. The signal processing perspective highlights new directions of research and facilitates the exchange of potentially beneficial ideas, both for the EnKF and high-dimensional nonlinear and non-Gaussian filtering in general.

Passive magnetic sensors measure the magnetic field density in three axes and are often integrated on a single chip. These low-cost sensors are widely used in car navigation as well as in battery powered navigation equipment such as smartphones as part of an electronic compass. We focus on a train localization application with multiple, exclusively onboard sensors and a track map. This approach is considered as a base technology for future railway applications such as collision avoidance systems or autonomous train driving. In this paper, we address the following question: how beneficial are passive magnetic measurements for train localization? We present and analyze measurements of two different magnetometers recorded on a regional train at regular passenger service. We show promising correlations of the measurements with the track positions and the traveled switch way. The processed data reveals that the railway environment has repeatable, location-dependent magnetic signatures. This is considered as a novel approach to train localization, as the use of these magnetic signals at first view is not obvious. The proposed methods based on passive magnetic measurements show a high potential to be integrated in new and existing train localization approaches.

Devising the planar routes of minimal length that are required to pass through predefined neighborhoods of target points plays an important role in reducing the missions operating cost. Two versions of the problem are considered. The first one assumes that the ordering of the targets is fixed a priori. In such a case, the optimal route is devised by solving a convex optimization problem formulated either as a second-order cone program or as a sum-of-squares optimization problem. Additional route properties, such as continuity and minimal curvature, are considered as well. The second version allows the ordering of the targets to be optimized to further reduce the route length. We show that such a problem can be solved by introducing additional binary variables, which allows the route to be designed using off-the-shelf mixed-integer solvers. A case study that shows that the proposed strategy is computationally tractable is presented.

This paper proposes a decentralized control strategy for the voltage regulation of islanded inverter-interfaced microgrids. We show that an inverter-interfaced microgrid under plug-and-play (PnP) functionality of distributed generations (DGs) can be cast as a linear time-invariant system subject to polytopic-type uncertainty. Then, by virtue of this novel description and use of the results from theory of robust control, the microgrid control system guarantees stability and a desired performance even in the case of PnP operation of DGs. The robust controller is a solution of a convex optimization problem. The main properties of the proposed controller are that: 1) it is fully decentralized and local controllers of DGs that use only local measurements; 2) the controller guarantees the stability of the overall system; 3) the controller allows PnP functionality of DGs in microgrids; and 4) the controller is robust against microgrid topology change. Various case studies, based on time-domain simulations in MATLAB/SimPowerSystems Toolbox, are carried out to evaluate the performance of the proposed control strategy in terms of voltage tracking, microgrid topology change, PnP capability features, and load changes.

A common issue with many system identification problems is that the true input to the system is unknown. This paper extends a previously presented indirect modelling framework that deals with identification of systems where the input is partially or fully unknown. In this framework, unknown inputs are eliminated by using additional measurements that directly or indirectly contain information about the unknown inputs. The resulting indirect predictor model is only dependent on known and measured signals and can be used to estimate the desired dynamics or properties. Since the input of the indirect model contains both known inputs and measurements that could all be correlated with the same disturbances as the output, estimation of the indirect model has similar challenges as a closed-loop estimation problem. In fact, due to the generality of the indirect modelling framework, it unifies a number of already existing system identification problems that are contained as special cases. For completeness, the paper is concluded with one method that can be used to estimate the indirect model as well as an experimental verification to show the applicability of the framework.

Most navigation systems today rely on global navigation satellite systems (gnss), including in cars. With support from odometry and inertial sensors, this is a sufficiently accurate and robust solution, but there are future demands. Autonomous cars require higher accuracy and integrity. Using the car as a sensor probe for road conditions in cloud-based services also sets other kind of requirements. The concept of the Internet of Things requires stand-alone solutions without access to vehicle data. Our vision is a future with both invehicle localization algorithms and after-market products, where the position is computed with high accuracy in gnss-denied environments. We present a localization approach based on a prior that vehicles spend the most time on the road, with the odometer as the primary input. When wheel speeds are not available, we present an approach solely based on inertial sensors, which also can be used as a speedometer. The map information is included in a Bayesian setting using the particle filter (PF) rather than standard map matching. In extensive experiments, the performance without gnss is shown to have basically the same quality as utilizing a gnss sensor. Several topics are treated: virtual measurements, dead reckoning, inertial sensor information, indoor positioning, off-road driving, and multilevel positioning.

There is a big trend nowadays to use event-triggered proximity report for indoor positioning. This paper presents a generic received-signal-strength (RSS) threshold optimization framework for generating informative proximity reports. The proposed framework contains five main building blocks, namely the deployment information, RSS model, positioning metric selection, optimization process and management. Among others, we focus on Gaussian process regression (GPR)-based RSS models and positioning metric computation. The optimal RSS threshold is found through minimizing the best achievable localization root-mean-square-error formulated with the aid of fundamental lower bound analysis. Computational complexity is compared for different RSS models and different fundamental lower bounds. The resulting optimal RSS threshold enables enhanced performance of new fashioned low-cost and low-complex proximity report-based positioning algorithms. The proposed framework is validated with real measurements collected in an office area where bluetooth-low-energy (BLE) beacons are deployed.

In this paper, we propose a distributed algorithm for solving coupled problems with chordal sparsity or an inherent tree structure which relies on primal–dual interior-point methods. We achieve this by distributing the computations at each iteration, using message-passing. In comparison to existing distributed algorithms for solving such problems, this algorithm requires far fewer iterations to converge to a solution with high accuracy. Furthermore, it is possible to compute an upper-bound for the number of required iterations which, unlike existing methods, only depends on the coupling structure in the problem. We illustrate the performance of our proposed method using a set of numerical examples.

This paper presents a novel multi-sensor framework to efficiently identify, track, localise and map every piece of fruit in a commercial mango orchard. A multiple viewpoint approach is used to solve the problem of occlusion, thus avoiding the need for labour-intensive field calibration to estimate actual yield. Fruit are detected in images using a state-of-the-art faster R-CNN detector, and pair-wise correspondences are established between images using trajectory data provided by a navigation system. A novel LiDAR component automatically generates image masks for each canopy, allowing each fruit to be associated with the corresponding tree. The tracked fruit are triangulated to locate them in 3D, enabling a number of spatial statistics per tree, row or orchard block. A total of 522 trees and 71,609 mangoes were scanned on a Calypso mango orchard near Bundaberg, Queensland, Australia, with 16 trees counted by hand for validation, both on the tree and after harvest. The results show that single, dual and multi-view methods can all provide precise yield estimates, but only the proposed multi-view approach can do so without calibration, with an error rate of only 1.36% for individual trees.

1 Migratory songbirds carry an inherited capacity to migrate several thousand kilometers each year crossing continental landmasses and barriers between distant breeding sites and wintering areas. How individual songbirds manage with extreme precision to find their way is still largely unknown. The functional characteristics of biological compasses used by songbird migrants has mainly been investigated by recording the birds directed migratory activity in circular cages, so-called Emlen funnels. This method is 50 years old and has not received major updates over the past decades. The aim of this work was to compare the results from newly developed digital methods with the established manual methods to evaluate songbird migratory activity and orientation in circular cages. 2 We performed orientation experiments using the European robin (Erithacus rubecula) using modified Emlen funnels equipped with thermal paper and simultaneously recorded the songbird movements from above. We evaluated and compared the results obtained with five different methods. Two methods have been commonly used in songbirds orientation experiments; the other three methods were developed for this study and were based either on evaluation of the thermal paper using automated image analysis, or on the analysis of videos recorded during the experiment. 3 The methods used to evaluate scratches produced by the claws of birds on the thermal papers presented some differences compared with the video analyses. These differences were caused mainly by differences in scatter, as any movement of the bird along the sloping walls of the funnel was recorded on the thermal paper, whereas video evaluations allowed us to detect single takeoff attempts by the birds and to consider only this behavior in the orientation analyses. Using computer vision, we were also able to identify and separately evaluate different behaviors that were impossible to record by the thermal paper. 4 The traditional Emlen funnel is still the most used method to investigate compass orientation in songbirds under controlled conditions. However, new numerical image analysis techniques provide a much higher level of detail of songbirds migratory behavior and will provide an increasing number of possibilities to evaluate and quantify specific behaviors as new algorithms will be developed.

Geometric phases describe how in a continuous-time dynamical system the displacement of a variable (called phase variable) can be related to other variables (shape variables) undergoing a cyclic motion, according to an area rule. The aim of this paper is to show that geometric phases can exist also for discrete-time systems, and even when the cycles in shape space have zero area. A context in which this principle can be applied is stock trading. A zero-area cycle in shape space represents the type of trading operations normally carried out by high-frequency traders (entering and exiting a position on a fast time-scale), while the phase variable represents the cash balance of a trader. Under the assumption that trading impacts stock prices, even zero-area cyclic trading operations can induce geometric phases, i.e., profits or losses, without affecting the stock quote.

Background: The mode of action of a drug on its targets can often be classified as being positive (activator, potentiator, agonist, etc.) or negative (inhibitor, blocker, antagonist, etc.). The signed edges of a drug-target network can be used to investigate the combined mechanisms of action of multiple drugs on the ensemble of common targets. Results: In this paper it is shown that for the signed human drug-target network the majority of drug pairs tend to have synergistic effects on the common targets, i.e., drug pairs tend to have modes of action with the same sign on most of the shared targets, especially for the principal pharmacological targets of a drug. Methods are proposed to compute this synergism, as well as to estimate the influence of the drugs on the side effect of another drug. Conclusions: Enriching a drug-target network with information of functional nature like the sign of the interactions allows to explore in a systematic way a series of network properties of key importance in the context of computational drug combinatorics.

Inspired by ideas taken from the machine learning literature, new regularization techniques have been recently introduced in linear system identification. In particular, all the adopted estimators solve a regularized least squares problem, differing in the nature of the penalty term assigned to the impulse response. Popular choices include atomic and nuclear norms (applied to Hankel matrices) as well as norms induced by the so called stable spline kernels. In this paper, a comparative study of estimators based on these different types of regularizers is reported. Our findings reveal that stable spline kernels outperform approaches based on atomic and nuclear norms since they suitably embed information on impulse response stability and smoothness. This point is illustrated using the Bayesian interpretation of regularization. We also design a new class of regularizers defined by "integral" versions of stable spline/TC kernels. Under quite realistic experimental conditions, the new estimators outperform classical prediction error methods also when the latter are equipped with an oracle for model order selection. (C) 2016 Elsevier Ltd. All rights reserved.

It is a well-known fact that externally positive linear systems may fail to have a minimal positive realization. In order to investigate these cases, we introduce the notion of minimal eventually positive realization, for which the state update matrix becomes positive after a certain power. Eventually positive realizations capture the idea that in the impulse response of an externally positive system the state of a minimal realization may fail to be positive, but only transiently. As a consequence, we show that in discrete-time it is possible to use downsampling to obtain minimal positive realizations matching decimated sequences of Markov coefficients of the impulse response. In continuous-time, instead, if the sampling time is chosen sufficiently long, a minimal eventually positive realization leads always to a sampled realization which is minimal and positive.

In this letter, numerical algorithms for computing the marginal version of the Bayesian Cramér–Rao bound (M-BCRB) for jump Markov nonlinear systems and jump Markov linear Gaussian systems are proposed. Benchmark examples for both systems illustrate that the M-BCRB is tighter than three other recently proposed BCRBs

In this work we present a practical calibration algorithm that calibrates a magnetometer using inertial sensors. The calibration corrects for magnetometer sensor errors, for the presence of magnetic disturbances and for misalignment between the magnetometer and the inertial sensor axes. It is based on a maximum likelihood formulation and is formulated as an offline method. It is shown to give good results using data from two different commercially available sensor units. Using the calibrated magnetometer measurements in combination with the inertial sensors to determine orientation, is shown to lead to significantly improved heading estimates.

This study presents a new approach for optimal placement of synchronised phasor measurement units (PMUs) to ensure complete power system observability in the presence of non-synchronous conventional measurements and zero injections. Currently, financial or technical restrictions prohibit the deployment of PMUs on every bus, which in turn motivates their strategic placement across the power system. PMU allocation is optimised here based on measurement observability criteria for achieving solvability of the power system state estimation. Most of the previous work has proposed topological observability based methods for optimal PMU placement (OPP), which may not always ensure numerical observability required for successful execution of state estimation. The proposed OPP method finds out the minimum number and the optimal locations of PMUs required to make the power system numerically observable. The problem is formulated as a binary semi-definite programming (BSDP) model, with binary decision variables, minimising a linear objective function subject to linear matrix inequality observability constraints. The BSDP problem is solved using an outer approximation scheme based on binary integer linear programming. The developed method is conducted on IEEE standard test systems. A large-scale system with 3120 buses is also analysed to exhibit the applicability of proposed model to practical power system cases.

In simple organisms like E. coli, the metabolic response to an external perturbation passes through a transient phase in which the activation of a number of latent pathways can guarantee survival at the expenses of growth. Growth is gradually recovered as the organism adapts to the new condition. This adaptation can be modeled as a process of repeated metabolic adjustments obtained through the resilencings of the non-essential metabolic reactions, using growth rate as selection probability for the phenotypes obtained. The resulting metabolic adaptation process tends naturally to steer the metabolic fluxes towards high growth phenotypes. Quite remarkably, when applied to the central carbon metabolism of E. coli, it follows that nearly all flux distributions converge to the flux vector representing optimal growth, i.e., the solution of the biomass optimization problem turns out to be the dominant attractor of the metabolic adaptation process.

We present an adaptive smoother for linear state-space models with unknown process and measurement noise covariances. The proposed method utilizes the variational Bayes technique to perform approximate inference. The resulting smoother is computationally efficient, easy to implement, and can be applied to high dimensional linear systems. The performance of the algorithm is illustrated on a target tracking example.

Micro-Doppler radar signatures have great potential for classifying pedestrians and animals, as well as their motion pattern, in a variety of surveillance applications. Due to the many degrees of freedom involved, real data need to be complemented with accurate simulated radar data to be able to successfully design and test radar signal processing algorithms. In many cases, the ability to collect real data is limited by monetary and practical considerations, whereas in a simulated environment, any desired scenario may be generated. Motion capture (MOCAP) has been used in several works to simulate the human micro-Doppler signature measured by radar; however, validation of the approach has only been done based on visual comparisons of micro-Doppler signatures. This work validates and, more importantly, extends the exploitation of MOCAP data not just to simulate micro-Doppler signatures but also to use the simulated signatures as a source of a priori knowledge to improve the classification performance of real radar data, particularly in the case when the total amount of data is small.

Synthetic aperture radar (SAR) equipment is a radar imaging system that can be used to create high-resolution images of a scene by utilizing the movement of a flying platform. Knowledge of the platforms trajectory is essential to get good and focused images. An emerging application field is real-time SAR imaging using small and cheap platforms where estimation errors in navigation systems imply unfocused images. This contribution investigates a joint estimation of the trajectory and SAR image. Starting with a nominal trajectory, we successively improve the image by optimizing a focus measure and updating the trajectory accordingly. The method is illustrated using simulations using typical navigation performance of an unmanned aerial vehicle. One real data set is used to show feasibility, where the result indicates that, in particular, the azimuth position error is decreased as the image focus is iteratively improved.

An established method for grey-box identification is to use maximum-likelihood estimation for the nonlinear case implemented via extended Kalman filtering. In applications of (nonlinear) model predictive control a more and more common approach for the state estimation is to use moving horizon estimation, which employs (nonlinear) optimization directly on a model for a whole batch of data. This paper shows that, in the linear case, horizon estimation may also be used for joint parameter estimation and state estimation, as long as a bias correction based on the Kalman filter is included. For the nonlinear case two special cases are presented where the bias correction can be determined without approximation. A procedure how to approximate the bias correction for general nonlinear systems is also outlined. (C) 2015 Elsevier Ltd. All rights reserved.

In this paper, we present an approach to combine measurements from inertial sensors (accelerometers and gyroscopes) with time-of-arrival measurements from an ultrawideband (UWB) system for indoor positioning. Our algorithm uses a tightly coupled sensor fusion approach, where we formulate the problem as a maximum a posteriori (MAP) problem that is solved using an optimization approach. It is shown to lead to accurate 6-D position and orientation estimates when compared to reference data from an independent optical tracking system. To be able to obtain position information from the UWB measurements, it is imperative that accurate estimates of the UWB receivers positions and their clock offsets are available. Hence, we also present an easy-to-use algorithm to calibrate the UWB system using a maximum-likelihood (ML) formulation. Throughout this work, the UWB measurements are modeled by a tailored heavy-tailed asymmetric distribution to account for measurement outliers. The heavy-tailed asymmetric distribution works well on experimental data, as shown by analyzing the position estimates obtained using the UWB measurements via a novel multilateration approach.

We present an online method for joint state and parameter estimation in jump Markov non-linear systems (JMNLS). State inference is enabled via the use of particle filters which makes the method applicable to a wide range of non-linear models. To exploit the inherent structure of JMNLS, we design a Rao-Blackwellized particle filter (RBPF) where the discrete mode is marginalized out analytically. This results in an efficient implementation of the algorithm and reduces the estimation error variance. The proposed RBPF is then used to compute, recursively in time, smoothed estimates of complete data sufficient statistics. Together with the online expectation maximization algorithm, this enables recursive identification of unknown model parameters including the transition probability matrix. The method is also applicable to online identification of jump Markov linear systems(JMLS). The performance of the method is illustrated in simulations and on a localization problem in wireless networks using real data.

 It is well-known that the motion of an acoustic source can be estimated from Doppler shift observations. It is however not obvious how to design a sensor network to efficiently deliver the localization service. In this work a rather simplistic motion model is proposed that is aimed at sensor networks with realistic numbersof sensor nodes. It is also described how to efficiently solve the associated least squares optimization problem by Gauss-Newton variable projection techniques, and how to initiate the numerical search from simple features extracted from the observed frequency series. The methods are evaluated by Monte Carlo simulations and demonstrated on real data by localizing an all-terrain vehicle. Itis concluded that the processing components included are fairly mature for practical implementations in sensor networks.

In this letter, we propose a general framework for greedy reduction of mixture densities of exponential family. The performances of the generalized algorithms are illustrated both on an artificial example where randomly generated mixture densities are reduced and on a target tracking scenario where the reduction is carried out in the recursion of a Gaussian inverse Wishart probability hypothesis density (PHD) filter.

In this paper, we consider convex feasibility problems (CFPs) where the underlying sets are loosely coupled, and we propose several algorithms to solve such problems in a distributed manner. These algorithms are obtained by applying proximal splitting methods to convex minimization reformulations of CFPs. We also put forth distributed convergence tests which enable us to establish feasibility or infeasibility of the problem distributedly, and we provide convergence rate results. Under the assumption that the problem is feasible and boundedly linearly regular, these convergence results are given in terms of the distance of the iterates to the feasible set, which are similar to those of classical projection methods. In case the feasibility problem is infeasible, we provide convergence rate results that concern the convergence of certain error bounds.

Prediction and filtering of continuous-time stochastic processes often require a solver of a continuous-time differential Lyapunov equation (CDLE), for example the time update in the Kalman filter. Even though this can be recast into an ordinary differential equation (ODE), where standard solvers can be applied, the dominating approach in Kalman filter applications is to discretize the system and then apply the discrete-time difference Lyapunov equation (DDLE). To avoid problems with stability and poor accuracy, oversampling is often used. This contribution analyzes over-sampling strategies, and proposes a novel low-complexity analytical solution that does not involve oversampling. The results are illustrated on Kalman filtering problems in both linear and nonlinear systems.

With the electromagnetic theory as basis, we present a sensor model for three-axis magnetometers suitable for localization and tracking as required in intelligent transportation systems and security applications. The model depends on a physical magnetic dipole model of the target and its relative position to the sensor. Both point target and extended target models are provided as well as a target orientation dependent model. The suitability of magnetometers for tracking is analyzed in terms of local observability and the Cramér Rao lower bound as a function of the sensor positions in a two sensor scenario. The models are validated with real field test data taken from various road vehicles which indicate excellent localization as well as identification of the magnetic target model suitable for target classification. These sensor models can be combined with a standard motion model and a standard nonlinear filter to track metallic objects in a magnetometer network.

Localization is an enabling technology in many applications and services today and in the future. Satellite navigation often works fine for navigation, infotainment and location based services, and it is today the dominating solution in commercial products. A nice exception is the localization in Google Maps, where radio signal strength from WiFi and cellular networks are used as complementary information to increase accuracy and integrity. With the on-going trend with more autonomous functions being introduced in our vehicles and with all our connected devices, most of them operated in indoor enviroments where satellite signals are not available,there is an acute need for new solutions.

At the same time, our smartphones are getting more sophisticated in their sensor configuration. Therefore, in this chapter we present a freely available Sensor Fusion app developed in-house, how it works, how it has been used, and how it can be used based on a variety of applications in our research and student projects.

It is a well-known fact that exercising helps people improve their overall well-being; both physiological and psychological health. Regular moderate physical activity improves the risk of disease progression, improves the chances for successful rehabilitation, and lowers the levels of stress hormones. Physical fitness can be categorized in cardiovascular fitness, and muscular strength and endurance. A proper balance between aerobic activities and strength exercises are important to maximize the positive effects. This balance is not always easily obtained, so assistance tools are important. Hence, ambient assisted living (AAL) systems that support and motivate balanced training are desirable. This chapter presents methods to provide this, focusing on the methodologies and concepts implemented by the authors in the physical activity monitoring for aging people (PAMAP) platform. The chapter sets the stage for an architecture to provide personalized activity monitoring using a network of wearable sensors, mainly inertial measurement units (IMU). The main focus is then to describe how to do this in a personalizable way: (1) monitoring to provide an estimate of aerobic activities performed, for which a boosting based method to determine activity type, intensity, frequency, and duration is given; (2) supervise and coach strength activities. Here, methodologies are described for obtaining the parameters needed to provide real-time useful feedback to the user about how to exercise safely using the right technique.

In this chapter parallel implementations of hybrid MPC will be discussed. Different methods for achieving parallelism at different levels of the algorithms will be surveyed. It will be seen that there are many possible ways of obtaining parallelism for hybrid MPC, and it is by no means clear which possibilities that should be utilized to achieve the best possible performance. To answer this question is a challenge for future research.

In almost all parts of the world, breast cancer is one of the major causes of death among women. But at the same time, it is one of the most curable cancers if it is diagnosed at early stage. This paper tries to find a model that diagnose and classify breast cancer with high accuracy and help to both patients and doctors in the future. Here we develop a model using Normalized Multi Layer Perceptron Neural Network to classify breast cancer with high accuracy. The results achieved is very good (accuracy is 99.27%). It is very promising result compared to previous researches where Artificial Neural Networks were used. As benchmark test, Breast Cancer Wisconsin (Original) was used.

In urban environments, cellular network-based positioning of user equipment (UE) is a challenging task, especially in frequently occurring non-line-of-sight (NLOS) conditions. This paper investigates the use of two machine learning methods – neural networks and random forests – to estimate the position of UE in NLOS using best received reference signal beam power measurements. We evaluated the suggested positioning methods using data collected from a fifth-generation cellular network (5G) testbed provided by Ericsson. A statistical test to detect NLOS conditions with a probability of detection that is close to 90% is suggested. We show that knowledge of the antenna are crucial for accurate position estimation. In addition, our results show that even with a limited set of training data and one 5G transmission point, it is possible to position UE within 10 meters with 80% accuracy.

The CDIO Syllabus survey has successfully been applied to the Bachelor’s and Master’s programs in Experimental and Medical Biosciences, within the Faculty of Medicine and Health Sciences at Linköping University, Sweden. The programs are and have been, subject to considerable redesign with strong influence from the CDIO framework. One of the main drivers for the redesign is a shift concerning the main job market after graduation, from an academic career to industry and healthcare. One of the steps in the development process has been to carry out a Syllabus survey based on an adapted version of the CDIO Syllabus. The survey was sent out to students and to various categories of professionals, and in total 87 responses were received. The adapted version of the Syllabus and the design, execution, and outcome of the survey is presented.

The CDIO framework is an integrated and important part of the new quality assurance system within the Faculty of Science and Engineering at Linköping University. Both the CDIO Syllabus and the CDIO Standards are used extensively in the system. First, the paper presents the development and use of the second generation of course matrices (previously denoted ITU-matrices) and program matrices, which build upon an adapted and extended version of the CDIO Syllabus. The extension is made to also include bachelor’s and master’s program in subjects outside the engineering field. Second, the paper presents how the CDIO Standards are used in the quality reports, which are vital parts of the quality assurance systems. As a result, the CDIO framework is used for the design, management, and quality assurance of all education programs ( approximately 60 programs) within the Faculty of Science and Engineering at Linköping University.

In this paper, tightness relations (or inequalities) between Bayesian lower bounds (BLBs) on the mean-squared-error are derived which result from the marginalization of a joint probability density function (pdf) depending on both parameters of interest and extraneous or nuisance parameters. In particular,it is shown that for a large class of BLBs, the BLB derived from the marginal pdf is at least as tight as the corresponding BLB derived from the joint pdf. A Bayesian linear regression example is used to illustrate the tightness relations

This paper considers the problem of gathering information about features of interest in adversarial environments using mobile robots equipped with sensors. The problem is formulated as an informative path planning problem where the objective is to maximize the gathered information while minimizing the tracking performance of the adversarial observer. The optimization problem, that at first glance seems intractable to solve to global optimality, is shown to be equivalent to a mixed-integer semidefinite program that can be solved to global optimality using off-the-shelf optimization tools.

This paper addresses the problem of retrieving consistentestimates in a distributed network where the communication between the nodes is constrained such that only the diagonal elements of the covariance matrix are allowed to be exchanged. Several methods are developed for preserving and/or recovering consistency under the constraints imposed by the communication protocol. The proposed methods are used in conjunction with the covariance intersection method and the estimation performance is evaluated based on information usage and consistency. The results show that among the proposed methods, consistency can be preserved equally well at the transmitting node as at the receiving node.

This paper investigates the problem of controlling a complex network with reduced control energy. Two centrality measures are defined, one related to the energy that a control, placed on a node, can exert on the entire network, and the other related to the energy that the network exerts on a node. We show that by combining these two centrality measures conflicting control energy requirements, like minimizing the average energy needed to steer the state in any direction and the energy needed for the worst direction, can be simultaneously taken into account. From an algebraic point of view, the node ranking that we obtain from the combination of our centrality measures is related to the non-normality of the adjacency matrix of the graph.

This paper proposes a method to generate informative trajectories for a mobile sensor that tracks agile targets.The goal is to generate a sensor trajectory that maximizes the tracking performance, captured by a measure of the covariance matrix of the target state estimate. The considered problem is acombination of estimation and control, and is often referred to as informative path planning (IPP). When using nonlinear sensors, the tracking performance depends on the actual measurements, which are naturally unavailable in the planning stage.The planning problem hence becomes a stochastic optimization problem, where the expected tracking performance is used inthe objective function. The main contribution of this work is anapproximation of the problem based on deterministic sampling of the predicted target distribution. This is in contrast to prior work, where only the most likely target trajectory is considered.It is shown that the proposed method greatly improves the ability to track agile targets, compared to a baseline approach.   

The number of sensors used in tracking scenarios is constantly increasing, this puts high demands on the tracking methods to handle these data streams. Central processing (ideally optimal) puts high demands on the central node, is sensitive to inaccurate sensor parameters, and suffers from the single point of failure problem. Decentralizing the tracking can improve this, but may give considerable performance loss. The newly presented inverse covariance intersection method, proven to be consistent, even under unknown track cross-correlations, is benchmarked against alternatives. Different track-to-track methods, including smoothed association over a window, are compared. A scenario with objects tracked in multiple cameras, not necessarily optimized for tracking, are used to give realism to the evaluations.

We present a technique for kernel-based identification of Wiener systems. We model the impulse response of the linear block with a Gaussian process. The static nonlinearity is modeled with a combination of basis functions. The coefficients of the static nonlinearity are estimated, together with the hyperparameters of the covariance function of the Gaussian process model, using an iterative algorithm based on the expectation-maximization method combined with elliptical slice sampling to sample from the posterior distribution of the impulse response given the data. The same sampling method is then used to find the posterior-mean estimate of the impulse response. We test the proposed algorithm on a benchmark of randomly-generated Wiener systems.

In Massive MIMO, the pilot contamination effect reduces the spectral efficiency (SE) gains and superimposed pilot (SP) transmission has been proposed to mitigate this effect. SP is based on transmitting pilot and data symbols simultaneously to allow for longer pilots and no pilot overhead. This work studies the optimal power control strategies in the uplink of a Massive MIMO system with SP and detection based on maximum ratio combining The optimization objectives arc maximum product of SINRs and max-min fairness, and these problems are reformulated as geometric programs which allow for efficient implementations. The numerical results indicate that the SE gains from the optimal power control with respect to the heuristic statistical channel inversion power control, are more significant when the interference from pilot symbols is subtracted before data detection.

The main idea in this paper is to implement a distributed primal-dual interior-point algorithm for loosely coupled Quadratic Programming problems. We implement this in Julia and show how can we exploit parallelism in order to increase the computational speed. We investigate the performance of the algorithm on a Model Predictive Control problem.

In this work we consider a nonlinear interconnected system describing a decision-making process in a community of agents characterized by the coexistence of collaborative and antagonistic interactions. The resulting signed graph is in general not structurally balanced. It is shown in the paper that the decision-making process is affected by the frustration of the signed graph, in the sense that a nontrivial decision can be reached only if the social commitment of the agents is high enough to win the disorder introduced by the frustration in the network. The higher the frustration of the graph, the higher the commitment strength required from the agents.

We explore the problem of identification of LPV models when the scheduling variables are not known in advance and the model parameters exhibit a dynamic dependence on them. We consider an affine ARX model structure whose parameters vary with time. We solve for the models parameters and scheduling variables in two steps. In the first step, we use the measured input-output data to realize a parameter trajectory by solving a regularized Hankel matrix rank minimization problem. The regularization penalty is guided by the prior knowledge regarding the nature of systems time variation. In the second step, the scheduling variables are estimated as parameters of a sparse ARX structure relating the models parameters to the measured input-output variables. The effectiveness of the proposed approach is illustrated with two practical examples. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

The demand for fuel-efficient transport solutions are steadily increasing with the goal of reducing environmental impact and increasing efficiency. Heavy-Duty Vehicle (HDV) platooning is a promising concept where multiple HDVs drive together in a convoy with small intervehicular spacing. By doing this, the aerodynamic drag is reduced which in turn lowers fuel consumption. We propose a novel Model Predictive Control (MPC) framework for longitudinal control of the follower vehicle in a platoon consisting of two HDVs when no vehicle-to-vehicle communication is available. In the framework, the preceding vehicle's velocity profile is predicted using artificial neural networks which uses a topographic map of the road as input and is trained offline using synthetic data. The gear shifting and mass of consumed fuel for the controlled follower vehicle is modeled and used within the MPC controller. The efficiency of the proposed framework is verified in simulation examples and is benchmarked with a currently available control solution.  

In this work, we study the multiple kernel based regularized system identification with the hyper-parameter estimated by using the Steins unbiased risk estimators (SURE). To approach the problem, a QR factorization is first employed to compute SUREs objective function and its gradient in an efficient and accurate way. Then we propose an algorithm to solve the SURE problem, which contains two parts: the outer optimization part and the inner optimization part. For the outer optimization part, the coordinate descent algorithm is used and for the inner optimization part, the projection gradient algorithm is used. Finally, the efficacy of the proposed algorithm is demonstrated by numerical simulations. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

In this paper, we study the asymptotic properties of the generalized cross validation (GCV) hyperparameter estimator and establish its connection with the Steins unbiased risk estimators (SURE) as well as the mean squared error (MSE). It is shown that as the number of data goes to infinity, the GCV has the same asymptotic property as the SURE does and both of them converge to the best hyperparameter in the MSE sense. We illustrate the efficacy of the result by Monte Carlo simulations. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Because of the increased demand on fault detection, monitoring and predictive maintenance, online or recursive identification is playing a more important role in systems engineering. In the recent releases of System Identification Toolbox (TM) for MATLAB (R), this has been reflected in a more substantial support for online techniques. This contribution gives an account of these improvements. It covers the addition of nonlinear filtering algorithms, such as the extended Kalman filter, the unscented Kalman filter and particle filters. The traditional recursive estimation techniques for polynomial models have also been enhanced with a more versatile syntax. Several new Simulink (R) blocks have been developed to support Simulink (R) models with online estimation. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Using Maximum Likelihood (or Prediction Error) methods to identify linear state space model is a prime technique. The likelihood function is a nonconvex function and care must be exercised in the numerical maximization. Here the focus will be on affine parameterizations which allow some special techniques and algorithms. Three approaches to formulate and perform the maximization are described in this contribution: (1) The standard and well known Gauss Newton iterative search, (2) a scheme based on the EM (expectation-maximization) technique, which becomes especially simple in the affine parameterization case, and (3) a new approach based on lifting the problem to a higher dimension in the parameter space and introducing rank constraints. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

This paper concerns the estimation of a dynamic model from two measured signals when it is not clear which signal should be used as input to the model. In this case, both a forward and an inverse model can be estimated. Here, a basic instrumental variable approach is used and it is shown that the forward and inverse model estimators give identical parameter estimates provided that corresponding model structures have been used. Furthermore, it is shown that this scenario occurs when properties of a quadcopter are estimated from accelerometer and gyro signals and, hence, that it does not matter which signal is used as input.

In this paper we show how to synthesize simple explicit MPC controllers based on approximate dynamic programming. Here, a given MPC optimization problem over a finite horizon is solved iteratively as a series of problems of size one. The optimal cost function of each subproblem is approximated by a quadratic function that serves as a cost-to-go function for the subsequent iteration. The approximation is designed in such a way that closed-loop stability and recursive feasibility is maintained. Specifically, we show how to employ sum-of-squares relaxations to enforce that the approximate cost-to-go function is bounded from below and from above for all points of its domain. By resorting to quadratic approximations, the complexity of the resulting explicit MPC controller is considerably reduced both in terms of memory as well as the on-line computations. The procedure is applied to control an inverted pendulum and experimental data are presented to demonstrate viability of such an approach.

In this paper, Bayesian lower bounds (BLBs) are obtained via a general form of the Pythagorean theorem where the inner product derives from the joint or the a-posteriori probability density function (pdf). When joint pdf is considered, the BLBs obtained encompass the Weiss-Weinstein family (WWF). When a-posteriori pdf is considered, by resorting to an embedding between two ad hoc subspaces, it is shown that any ”standard” BLBs of the WWF admits a ”tighter” form which upper bounds the ”standard” form. Interestingly enough, this latter result may explain why the ”standard” BLBs of the WWF are not always as tight as expected, as exemplified in the case of the Bayesian Cram´er-Rao Bound. As a consequence an updated definition of efficiency is proposed, as well as the introduction of an updated class of efficient estimators.

This paper presents a systematic approach for computing local solutions to motion planning problems in non-convex environments using numerical optimal control techniques. It extends the range of use of state-of-the-art numerical optimal control tools to problem classes where these tools have previously not been applicable. Today these problems are typically solved using motion planners based on randomized or graph search. The general principle is to define a homotopy that transforms, or preferably relaxes, the original problem to an easily solved problem. In this work, it is shown that by combining a Sequential Quadratic Programming (SQP) method with a homotopy approach that gradually transforms the problem from a relaxed one to the original one, practically relevant locally optimal solutions to the motion planning problem can be computed. The approach is demonstrated in motion planning problems in challenging 2D and 3D environments, where the presented method significantly outperforms both a state-of-the-art numerical optimal control method and a state-of-the-art open-source optimizing sampling-based planner commonly used as benchmark. 

An experimental comparison of two feed-forward based frictioncompensation methods is presented. The first method is based on theLuGre friction model, using identified friction model parameters, andthe second method is based on B-spline network, where the networkweights are learned from experiments. The methods are evaluated andcompared via experiments using a six axis industrial robot carryingout circular movements of different radii. The experiments show thatthe learning-based friction compensation gives an error reduction ofthe same magnitude as for the LuGre-based friction compensation.

The number of sensors used in tracking scenariosis constantly increasing, this puts high demands on the trackingmethods to handle these data streams. Central processing (ideallyoptimal) puts high demands on the central node, is sensitive toinaccurate sensor parameters, and suffers from the single pointof failure problem. Decentralizing the tracking can improve this,but may give considerable performance loss. The newly presentedinverse covariance intersection method, proven to be consistent,even under unknown track cross correlations, is benchmarkedagainst alternatives. Different track-to-track methods, includingsmoothed association over a window, are compared. A scenariowith objects tracked in multiple cameras, not necessarily opti-mized for tracking, are used to give realism to the evaluations.

In this paper, marginal versions of the Bayesian Bhattacharyya lower bound (BBLB), which is a tighter alternative to the classical Bayesian Cramer-Rao bound, for discrete-time filtering are proposed. Expressions for the second and third-order marginal BBLBs are obtained and it is shown how these can be approximately calculated using particle filtering. A simulation example shows that the proposed bounds predict the achievable performance of the filtering algorithms better.

We report the results of a series of numerical studies examining the convergence rate for some approximate representations of alpha-stable distributions, which are a highly intractable class of distributions for inference purposes. Our proposed representation turns the intractable inference for an infinite-dimensional series of parameters into an (approximately) conditionally Gaussian representation, to which standard inference procedures such as Expectation-Maximization (EM), Markov chain Monte Carlo (MCMC) and Particle Filtering can be readily applied. While we have previously proved the asymptotic convergence of this representation, here we study the rate of this convergence for finite values of a truncation parameter, c. This allows the selection of appropriate truncations for different parameter configurations and for the accuracy required for the model. The convergence is examined directly in terms of cumulative distribution functions and densities, through the application of the Berry theorems and Parseval theorems. Our results indicate that the behaviour of our representations is significantly superior to that of representations that simply truncate the series with no Gaussian residual term.

With ever-increasing productivity targets in mining operations, there is a growing interest in mining automation. In future mines, remote controlled and autonomous haulers will operate underground guided by LiDAR (Light Detection And Ranging) sensors. We envision reusing LiDAR measurements to maintain accurate mine maps that would contribute to both safety and productivity. Extrapolating from a pilot project on reliable wireless communication in Bolidens Kankberg mine, we propose establishing a System-of-Systems (SoS) with LiDAR-equipped haulers and existing mapping solutions as constituent systems. SoS requirements engineering inevitably adds a political layer, as independent actors are stakeholders both on the system and SoS levels. We present four SoS scenarios representing different business models, discussing how development and operations could be distributed among Boliden and external stakeholders, e.g., the vehicle suppliers, the hauling company, and the developers of the mapping software. Based on eight key variation points, we compare the four scenarios from both technical and business perspectives. Finally, we validate our findings in a seminar with participants from the relevant stakeholders. We conclude that to determine which scenario is the most promising for Boliden, trade-offs regarding control, costs, risks, and innovation must be carefully evaluated.

In this paper we provide necessary conditions for the existence of multiple equilibrium points for a class of non-linear cooperative networked systems with saturating interactions which describe models of collective decision-making. The multiple steady states of the dynamics represent the possible outcomes of a decision process, and, except for one positive and one negative, have all mixed signs. The conditions we obtain can be formulated in terms of the algebraic connectivity of the network and are inspired by Perron-Frobenius arguments. It is also shown that the mixed-sign equilibria are contained in a ball of radius given by the norm of the positive equilibrium point and centered in the origin. Numerical examples are given to illustrate the results.

In this paper, we study the problem of controlling complex networks with unilateral controls, i.e., controls which can assume only positive or negative values, not both. Given a network with linear dynamics represented by the adjacency matrix A, we seek to understand the minimal number of unilateral controls that renders the network controllable. This problem has structural properties that for instance allows us to establish theoretical bounds and identify key topological properties that makes a network relatively easy to control with unilateral controls as compared to unrestricted controls. We find that the structure of the left null space of A is particularly important to this end. In a computational study we find that the network topology largely determines the number of unilateral controls and that the derived lower bounds often are achieved with heuristic methods. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Gray-box identification is prevalent in modeling physical and networked systems. However, due to the non-convex nature of the gray-box identification problem, good initial parameter estimates are crucial for a successful application. In this paper, a new identification method is proposed by exploiting the low-rank and structured Hankel matrix of impulse response. This identification problem is recasted into a difference-of-convex programming problem, which is then solved by the sequential convex programming approach with the associated initialization obtained by nuclear-norm optimization. The presented method aims to achieve the maximum impulse-response fitting while not requiring additional (non-convex) conditions to secure non-singularity of the similarity transformation relating the given state space matrices to the gray-box parameterized ones. This overcomes a persistent shortcoming in a number of recent contributions on this topic, and the new method can be applied for the structured state-space realization even if the involved system parameters are unidentifiable. The method can be used both for directly estimating the gray-box parameters and for providing initial parameter estimates for further iterative search in a conventional gray-box identification setup. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

The interest for system identification in dynamic networks has increased recently with a wide variety of applications. In many cases, it is intractable or undesirable to observe all nodes in a network and thus, to estimate the complete dynamics. Furthermore, it might even be challenging to estimate a subset of the network if key nodes are unobservable due to correlation between the nodes. In this contribution, we will discuss an approach to treat this problem. The approach relies on additional measurements that are dependent on the unobservable nodes and thus indirectly contain information about them. These measurements are used to form an alternative indirect model that is only dependent on observed nodes. The purpose of estimating this indirect model can be either to recover information about modules in the original network or to make accurate predictions of variables in the network. Examples are provided for both recovery of the original modules and prediction of nodes. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Motion planning for a general 2-trailer system poses a hard problem for any motion planning algorithm and previous methods have lacked any completeness or optimality guarantees. In this work we present a lattice-based motion planning framework for a general 2-trailer system that is resolution complete and resolution optimal. The solution will satisfy both differential and obstacle imposed constraints and is intended either as a part of an autonomous system or as a driver support system to automatically plan complicated maneuvers in backward and forward motion. The proposed framework relies on a precomputing step that is performed offline to generate a finite set of kinematically feasible motion primitives. These motion primitives are then used to create a regular state lattice that can be searched for a solution using standard graph-search algorithms. To make this graph-search problem tractable for real-time applications a novel parametrization of the reachable state space is proposed where each motion primitive moves the system from and to a selected set of circular equilibrium configurations. The approach is evaluated over three different scenarios and impressive real-time performance is achieved.

In this paper we provide necessary conditions for the existence of multiple equilibrium points for a class of non-linear cooperative networked systems with saturating interactions which describe models of collective decision-making. The multiple steady states of the dynamics represent the possible outcomes of a decision process, and, except for one positive and one negative, have all mixed signs. The conditions we obtain can be formulated in terms of the algebraic connectivity of the network and are inspired by Perron-Frobenius arguments. It is also shown that the mixed-sign equilibria are contained in a ball of radius given by the norm of the positive equilibrium point and centered in the origin. Numerical examples are given to illustrate the results.

In stable biological and ecological networks, the steady-state influence matrix gathers the signs of steady-state responses to step-like perturbations affecting the variables. Such signs are difficult to predict a priori, because they result from a combination of direct effects (deducible from the Jacobian of the network dynamics) and indirect effects. For stable monotone or cooperative networks, the sign pattern of the influence matrix can be qualitatively determined based exclusively on the sign pattern of the system Jacobian. For other classes of networks, we show that a semi-qualitative approach yields sufficient conditions for Jacobians with a given sign pattern to admit a fully positive influence matrix, and we also provide quantitative conditions for Jacobians that are translated eventually nonnegative matrices. We present a computational test to check whether the influence matrix has a constant sign pattern in spite of parameter variations, and we apply this algorithm to quasi-Metzler Jacobian matrices, to assess whether positivity of the influence matrix is preserved in spite of deviations from cooperativity. When the influence matrix is fully positive, we give a simple vertex algorithm to test robust stability. The devised criteria are applied to analyse the steady-state behaviour of ecological and biomolecular networks.

A signed network is a network with each link associated with a positive or negative sign. Models for nodes interacting over such signed networks, where two types of interactions are defined along the positive and negative links, respectively, arise from various biological, social, political, and economical systems. Starting from standard consensus dynamics, there are two basic types of negative interactions along negative links, namely state flipping or relative-state flipping. In this paper, we provide an algebraic-graphical method serving as a systematic tool of studying these dynamics over signed networks. Utilizing generalized Perron-Frobenius theory, graph theory, and elementary algebraic recursions, we show this method is useful to establish a series of basic convergence results for dynamics over signed networks.

The CDIO framework for development of engineering education is presented, including the overall ideas, the fundamental documents, and some development tools. The automatic control subject and its role in engineering education is studied using the CDIO Standards as reference. Some examples from the engineering education at Linkoping University are presented with special focus on the control education. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

This work evaluates a previously introduced algorithm called Particle-Based Rapid Incremental Smoother within the framework of state inference and parameter identification in Jump Markov Non-Linear System. It is applied to the recursive form of two well-known Maximum Likelihood based algorithms who face the common challenge of online computation of smoothed additive functionals in order to accomplish the task of model parameter estimation. This work extends our previous contributions on identification of Markovian switching systems with the goal to reduce the computational complexity. A benchmark problem is used to illustrate the results.

In this paper, an indoor vehicle multi-level positioning algorithm is proposed that makes use of an indoor map, as well as dead-reckoning sensor information that is available in every car. A particle filter framework is used for online optimal Bayesian vehicle positioning with indoor-outdoor transitions. The method is validated experimentally in two indoor multi-level car parks. The achieved results indicate that accurate indoor positioning is possible already today without relying on expensive technology such as e.g. laser scanners or additional hardware.

Narrowband Internet of Things (NB-IoT) is an emerging cellular technology designed to target low-cost devices, high coverage, long device battery life (more than ten years), and massive capacity. We investigate opportunities for device tracking in NB-IoT systems using Observed Time Difference of Arrival (OTDOA) measurements. Reference Signal Time Difference (RSTD) reports are simulated to be sent to the mobile location center periodically or on an ondemand basis. We investigate the possibility of optimizing the number of reports per minute budget on horizontal positioning accuracy using an on-demand reporting method based on the Signal to Noise Ratio (SNR) of the measured cells received by the User Equipment (UE). Wireless channels are modeled considering multipath fading propagation conditions. Extended Pedestrian A (EPA) and Extended Typical Urban (ETU) delay profiles corresponding to low and high delay spread environments, respectively, are simulated for this purpose. To increase the robustness of the filtering method, measurement noise outliers are detected using confidence bounds estimated from filter innovations.

This paper deals with state inference and parameter identification in Jump Markov Non-Linear System. The state inference problem is solved efficiently using a recently proposed Rao-Blackwellized Particle Filter, where the discrete state is integrated out analytically. Within the RBPF framework, Recursive Maximum Likelihood parameter identification is performed using gradient ascent algorithms. The proposed learning method has the advantage over (online) Expectation Maximization methods, that it can be easily applied to cases where the probability density functions defining the Jump Markov Non-Linear System are not members of the exponential family. Two benchmark problems illustrate the parameter identification performance.

In polar region operations, drift sea ice positioning and tracking is useful for both scientific and safety reasons. Modeling ice movements has proven difficult, not least due to the lack of information of currents and winds of high enough resolution. Thus, observations of drift ice is essential to an up-to-date ice-tracking estimate.

As an inverse problem, it is possible to extract current and wind estimates from the tracked objects of a Multi-Target Tracking (MTT) filter. By inserting the track estimates into a Gaussian field, we obtain a two-dimensional current estimate over a region of interest.

The algorithm is applied to a Terrestrial Radar Interferometer (TRI) dataset from Kongsfjorden, Svalbard, to show the practical application of the current estimation.

In this work, we estimate a model of the vertical dynamics of a quadcopter and explain how this model can be used for mass estimation and diagnosis of system changes. First, a standard thrust model describing the relation between the calculated control signals of the rotors and the thrust that is commonly used in literature is estimated. The estimation results are compared to those using a refined thrust model and it turns out that the refined model gives a significant improvement. The combination of a nonlinear model and closed-loop data poses some challenges and it is shown that an instrumental variables approach can be used to obtain accurate estimates. Furthermore, we show that the refined model opens up for fault detection of the quadcopter. More specifically, this model can be used for mass estimation and also for diagnosis of other parameters that might vary between and during missions.

The Military Institute of Engineering (IME) is a Brazilian Army higher education institution, located in Rio de Janeiro – Brazil. Every three years, the Brazilian government evaluates the engineering bachelor´s degree programs and IME is always among the best Brazilian engineering schools. Despite the excellent results, the CDIO framework was chosen, at the end of 2014, as a reference framework to improve the education process, reorganize the programs and promote the development of intra and interpersonal skills. The objective of this work is to show the initial steps of the CDIO  implementation. In the beginning of the process, the idea was to start only with the Mechanical Engineering program. However, after the initial promotion seminars presented for faculty, staff and students, a sense of commitment was created, and four of ten undergraduate programs also decided to participate. One of the first step was the formation of the team responsible to coordinate the implementation of this set of best practices. The Kotter framework has been used to drive this transformation process. The CDIO syllabus was translated into Portuguese, and it was compared with some of the Brazilian regulatory requirements for engineering education and has been used as a reference for future curriculum changes. This paper also highlights the process for change management, the remodeling of the first year and the introduction of an entrepreneurship course in partnership with an outstanding Brazilian business school.

In this paper, an approach to estimate the mass of a quadcopter using only inertial measurements and pilot commands is presented. For this purpose, a lateral dynamic model describing the relation between the roll rate and the lateral acceleration is formulated. Due to the quadcopter’s inherent instability, a controller is used to stabilize the system and the data is collected in closed loop. Under the effect of feedback and disturbances, the inertial measurements used as input and output are correlated with the disturbances, which complicates the parameter estimation. The parameters of the model are estimated using several methods. The simulation and experimental results show that the instrumental-variable method has the best potential to estimate the mass of the quadcopter in this setup.

This paper presents three iterative methods for orientation estimation. The first two are based on iterated Extended Kalman filter (IEKF) formulations with different state representations. The first is using the well-known unit quaternion as state (q-IEKF) while the other is using orientation deviation which we call IMEKF. The third method is based on nonlinear least squares (NLS) estimation of the angular velocity which is used to parametrise the orientation. The results are obtained using Monte Carlo simulations and the comparison is done with the non-iterative EKF and multiplicative EKF (MEKF) as baseline. The result clearly shows that the IMEKF and the NLS-based method are superior to q-IEKF and all three outperform the non-iterative methods.

Chronic kidney disease (CKD) is a global public health problem, affecting approximately 10% of the population worldwide. Yet, there is little direct evidence on how CKD can be diagnosed in a systematic and automatic manner. This paper investigates how CKD can be diagnosed by using machine learning (ML) techniques. ML algorithms have been a driving force in detection of abnormalities in different physiological data, and are, with a great success, employed in different classification tasks. In the present study, a number of different ML classifiers are experimentally validated to a real data set, taken from the UCI Machine Learning Repository, and our findings are compared with the findings reported in the recent literature. The results are quantitatively and qualitatively discussed and our findings reveal that the random forest (RF) classifier achieves the near-optimal performances on the identification of CKD subjects. Hence, we show that ML algorithms serve important function in diagnosis of CKD, with satisfactory robustness, and our findings suggest that RF can also be utilized for the diagnosis of similar diseases.

We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a PMCMC method based on an interacting pool of standard and conditional sequential Monte Carlo samplers. Like related methods, iPMCMC is a Markov chain Monte Carlo sampler on an extended space. We present empirical results that show significant improvements in mixing rates relative to both noninteracting PMCMC samplers and a single PMCMC sampler with an equivalent memory and computational budget. An additional advantage of the iPMCMC method is that it is suitable for distributed and multi-core architectures.

In order to meet the requirements for autonomous systems in real world applications, reliable path following controllers have to be designed to execute planned paths despite the existence of disturbances and model errors. In this paper we propose a Linear Quadratic controller for stabilizing a 2-trailer system with possible off-axle hitching around preplanned paths in backward motion. The controller design is based on a kinematic model of a general 2-trailer system including the possibility for off-axle hitching. Closed-loop stability is proved around a set of paths, typically chosen to represent the possible output from the path planner, using theory from linear differential inclusions. Using convex optimization tools a single quadratic Lyapunov function is computed for the entire set of paths.

The identification of continuous-time models of dynamical systems based on sampled measurements of input and output signals is a research topic that has received much attention during the past decades. However, a framework for the correct assessment of the performance of various estimation methods, as well as their numerical reliability, is still missing due to a number of benchmarking difficulties, equally applicable to both discrete-and continuous-time identification problems. This paper revisits this topic, reports new numerical results, highlights several fundamental aspects regarding the definition of an appropriate benchmark for the evaluation of continuous-time linear model identification algorithms and discusses several means of addressing the related existing problems.

In a continuous-time nonlinear driftless control system, a geometric phase is a consequence of nonintegrability of the vector fields, and it describes how cyclic trajectories in shape space induce non-periodic motion in phase space, according to an area rule. The aim of this paper is to shown that geometric phases exist also for discrete-time driftless nonlinear control systems, but that unlike their continuous-time counterpart, they need not obey any area rule, i.e., even zero-area cycles in shape space can lead to nontrivial geometric phases. When the discrete-time system is obtained through Euler discretization of a continuous-time system, it is shown that the zero-area geometric phase corresponds to the gap between the Euler discretization and an exact discretization of the continuous-time system.

When an aircraft is flying and burning fuel the center of gravity (c.g.) of the aircraft shifts slowly. The c.g. can also be shifted abruptly when e.g. a ghter aircraft releases a weapon. The shift in c.g. is difficult to measure or estimate so the flight control systems need to be robustly designed to cope with this variation. However for fighter aircrafts with high manoeuvrability there is room for improvements. In this paper we investigate if the use of adaptive control law augmentation can be used to better cope with the change in c.g. We augment a baseline controller with a robust Model Reference Adaptive Control (MRAC) design and analyse its benets and possible issues.

In many traffic situations there are times where interaction with other drivers is necessary and unavoidable in order to safely progress towards an intended destination. This is especially true for merge manoeuvres into dense traffic, where drivers sometimes must be somewhat aggressive and show the intention of merging in order to interact with the other driver and make the driver open the gap needed to execute the manoeuvre safely. Many motion planning frameworks for autonomous vehicles adopt a reactive approach where simple models of other traffic participants are used and therefore need to adhere to large margins in order to behave safely. However, the large margins needed can sometimes get the system stuck in congested traffic where time gaps between vehicles are too small. In other situations, such as a highway merge, it can be significantly more dangerous to stop on the entrance ramp if the gaps are found to be too small than to make a slightly more aggressive manoeuvre and let the driver behind open the gap needed. To remedy this problem, this work uses the Intelligent Driver Model (IDM) to explicitly model the interaction of other drivers and evaluates the risk by their required deceleration in a similar manner as the Minimum Overall Breaking Induced by Lane change (MOBIL) model that has been used in large scale traffic simulations before. This allows the algorithm to evaluate the effect on other drivers depending on our own trajectory plans by simulating the nearby traffic situation. Finding a globally optimal solution is often intractable in these situations so instead a large set of candidate trajectories are generated that are evaluated against the traffic scene by forward simulations of other traffic participants. By discretization and using an efficient trajectory generator together with efficient modelling of the traffic scene real-time demands can be met.

In multiparametric programming an optimization problem which is dependent on a parameter vector is solved parametrically. In control, multiparametric quadratic programming (mp-QP) problems have become increasingly important since the optimization problem arising in Model Predictive Control (MPC) can be cast as an mp-QP problem, which is referred to as explicit MPC. One of the main limitations with mp-QP and explicit MPC is the amount of memory required to store the parametric solution and the critical regions. In this paper, a method for exploiting low rank structure in the parametric solution of an mp-QP problem in order to reduce the required memory is introduced. The method is based on ideas similar to what is done to exploit low rank modifications in generic QP solvers, but is here applied to mp-QP problems to save memory. The proposed method has been evaluated experimentally, and for some examples of relevant problems the relative memory reduction is an order of magnitude compared to storing the full parametric solution and critical regions.

In this paper a cascaded approach for stabilizationand path tracking of a general 2-trailer vehicle configurationwith an off-axle hitching is presented. A low level LinearQuadratic controller is used for stabilization of the internalangles while a pure pursuit path tracking controller is used ona higher level to handle the path tracking. Piecewise linearityis the only requirement on the control reference which makesthe design of reference paths very general. A Graphical UserInterface is designed to make it easy for a user to design controlreferences for complex manoeuvres given some representationof the surroundings. The approach is demonstrated with challengingpath following scenarios both in simulation and on asmall scale test platform.

In this paper, recent results on the evaluation of the Bayesian Cramer-Rao bound for jump Markov systems are presented. In particular, previous work is extended to jump Markov systems where the discrete mode variable enters into both the process and measurement equation, as well as where it enters exclusively into the measurement equation. Recursive approximations are derived with finite memory requirements as well as algorithms for checking the validity of these approximations are established. The tightness of the bound and the validity of its approximation is investigated on a couple of examples.

We still have very little knowledge about how ourbrains decouple different sound sources, which is known assolving the cocktail party problem. Several approaches; includingERP, time-frequency analysis and, more recently, regression andstimulus reconstruction approaches; have been suggested forsolving this problem. In this work, we study the problem ofcorrelating of EEG signals to different sets of sound sources withthe goal of identifying the single source to which the listener isattending. Here, we propose a method for finding the number ofparameters needed in a regression model to avoid overlearning,which is necessary for determining the attended sound sourcewith high confidence in order to solve the cocktail party problem.

In inertial motion capture, a multitude of body segments are equipped with inertial sensors, consisting of 3D accelerometers and 3D gyroscopes. Using an optimization-based approach to solve the motion capture problem allows for natural inclusion of biomechanical constraints and for modeling the connection of the body segments at the joint locations. The computational complexity of solving this problem grows both with the length of the data set and with the number of sensors and body segments considered. In this work, we present a scalable and distributed solution to this problem using tailored message passing, capable of exploiting the structure that is inherent in the problem. As a proof-of-concept we apply our algorithm to data from a lower body configuration. 

Radar and sonar provide information of both range and radial velocity to unknown objects. This is accomplished by emitting a signal waveform and computing the round trip time and Doppler shift. Estimation of the round trip time and Doppler shift is usually done separately without considering the couplings between these two object related quantities. The purpose of this contribution is to first model the amplitude, time shift and time scale of the returned signal in terms of the object related states range and velocity, and analyse the Cramér-Rao lower bound (CRLB) for the joint range and velocity estimation problem. One of the conclusions is that there is negative correlation between range and velocity. The maximum likelihood (ML) cost function also confirms this strong negative correlation. For target tracking applications, the use of the correct covariance matrix for the measurement vector gives a significant gain in information, compared to using the variance of range and velocity assuming independence. In other words, the knowledge of the correlation tells the tracking filter that a too large range measurement comes most likely with a too small velocity measurement, and vice versa. Experiments with sound pulses reflected in a wall indoors confirm the main conclusion of negative correlation.

GPS is widely used for localization and tracking, however traditional GPS receivers consume too much energy for many applications. This paper implements and evaluates the performance of a low-energy GPS prototype. The main difference is that a traditional GPS needs to sample signals transmitted by satellites for 30 seconds to estimate its position. Our prototype reduces this time by three orders of magnitude and it can compute positions from only 2 milliseconds of data. We present a new algorithm that increases robustness by filtering on estimated residuals instead of using an altitude database. In addition, we show that our new algorithm works with both fixed and moving targets. The solution consists of (1) a portable device that samples the GPS signal and (2) a server that utilizes Doppler navigation and Coarse Time Navigation to estimate positions. We performed tests in a wide variety of environments and situations. These tests show that our prototype provides a median positioning error of roughly 40 meters even when the GPS receiver is moving at 80 kilometres per hour.

Positioning in cellular networks is often based on mobile-assisted measurements of serving and neighboring base stations. Traditionally, positioning is considered to be enabled when the mobile provides measurements of three different base stations. In this paper, we additionally investigate positioning based on time series of Time Of Flight (TOF) and Time Difference of Arrival (TDOA) measurements gathered from two base stations with known positions, where the specific base stations involved depend on the trajectory of the mobile station.. The set of two base stations is different along the trajectory. Each report contains TOF for the serving base station, and one TDOA measurement for the most favorable neighboring base station relative the serving base station. We derive explicit analytical solution related to the intersection of the absolute distance circle (from TOF) and relative distance hyperbola (from TDOA). We consider both geometric noise-free problem and the more realistic problem with additive noise as delivered in the 3rd Generation Partnership Project (3GPP) Long-Term Evolution (LTE). Positioning performance is evaluated using the Cramer-Rao lower bound.

This paper proposes a diagonal covariance matrix approximation for Wide-Sense Stationary (WSS) signals with correlated Gaussian noise. Existing signal models that incorporate correlations often require regularization of the covariance matrix, so that the covariance matrix can be inverted. The disadvantage of this approach is that matrix inversion is computational intensive and regularization decreases precision. We use Bienayme's theorem to approximate the covariance matrix by a diagonal one, so that matrix inversion becomes trivial, even with nonuniform rather than only uniform sampling that was considered in earlier work. This approximation reduces the computational complexity of the estimator and estimation bound significantly. We numerically validate this approximation and compare our approach with the Maximum Likelihood Estimator (MLE) and Cramer-Rao Lower Bound (CRLB) for multivariate Gaussian distributions. Simulations show that our approach differs less than 0.1% from this MLE and CRLB when the observation time is large compared to the correlation time. Additionally, simulations show that in case of non-uniform sampling, we increase the performance in comparison to earlier work by an order of magnitude. We limit this study to correlated signals in the time domain, but the results are also applicable in the space domain.

In Moving Horizon Estimation (MHE) the computed estimate is found by solving a constrained finite-time optimal estimation problem in real-time at each sample in a receding horizon fashion. The constrained estimation problem can be solved by, e.g., interior-point (IP) or active-set (AS) methods, where the main computational effort in both methods is known to be the computation of the search direction, i.e., the Newton step. This is often done using generic sparsity exploiting algorithms or serial Riccati recursions, but as parallel hardware is becoming more commonly available the need for parallel algorithms for computing the Newton step is increasing. In this paper a newly developed tailored, non-iterative parallel algorithm for computing the Newton step using the Riccati recursion for Model Predictive Control (MPC) is extended to MHE problems. The algorithm exploits the special structure of the Karush-Kuhn-Tucker system for the optimal estimation problem. As a result it is possible to obtain logarithmic complexity growth in the estimation horizon length, which can be used to reduce the computation time for IP and AS methods when applied to what is today considered as challenging estimation problems. Furthermore, promising numerical results have been obtained using an ANSI-C implementation of the proposed algorithm, which uses Message Passing Interface (MPI) together with InfiniBand and is executed on true parallel hardware. Beyond MHE, due to similarities in the problem structure, the algorithm can be applied to various forms of on-line and off-line smoothing problems.

This paper describes the motivation, the current state and the further actions of an improvement process of the engineering education at the Military Institute of Engineering (IME) in Brazil. Based on the reasons for why and how to change, the CDIO framework has been chosen as the kernel of this improvement process. The activities realized the plan of the further actions and the open questions are presented in this paper. The paper is a condensed presentation of the report (Cerqueira et. al., 2016), where a thorough background and more details can be found.

We study severely quantized received signal strength (RSS)-based cooperative localization in wireless sensor networks. We adopt the well-known sum-product algorithm over a wireless network (SPAWN) framework in our study. To address the challenge brought by severely quantized measurements, we adopt the principle of importance sampling and design appropriate proposal distributions. Moreover, we propose a parametric SPAWN in order to reduce both the communication overhead and the computational complexity. Experiments with real data corroborate that the proposed algorithms can achieve satisfactory localization accuracy for severely quantized RSS measurements. In particular, the proposed parametric SPAWN outperforms its competitors by far in terms of communication cost. We further demonstrate that knowledge about non-connected sensors can further improve the localization accuracy of the proposed algorithms.

The commercial interest in proximity services is increasing. Application examples include location-based information and advertisements, logistics, social networking, file sharing, etc. In this paper, we consider network-based positioning based on times series of proximity reports from a mobile device, either only a proximity indicator, or a vector of RSS from observed nodes. Such positioning corresponds to a latent and nonlinear observation model. To address these problems, we combine two powerful tools, namely particle filtering and Gaussian process regression (GPR) for radio signal propagation modeling. The latter also provides some insights into the spatial correlation of the radio propagation in the considered area. Radio propagation modeling and positioning performance are evaluated in a typical office area with Bluetooth-Low-Energy (BLE) beacons deployed for proximity detection and reports. Results show that the positioning accuracy can be improved by using GPR.

Drive cycle following is important for concept comparisons when evaluating vehicle concepts, but it can be time consuming to develop good driver models that can achieve accurate following of a specific velocity profile. Here, a new approach is proposed where a simple driver model based on a PID controller is extended with an Iterative Learning Control (ILC) algorithm. Simulation results using a nonlinear vehicle and control system model show that it is possible to achieve very good cycle following in a few iterations with little tuning effort. It is also possible to utilize the repetitive behavior in the drive cycle to accelerate the convergence of the ILC algorithm even further.

In this paper, we propose a new method to identify biochemical reaction networks (i.e. both reactions and kinetic parameters) from heterogeneous datasets. Such datasets can contain (a) data from several replicates of an experiment performed on a biological system; (b) data measured from a biochemical network subjected to different experimental conditions, for example, changes/perturbations in biological inductions, temperature, gene knock-out, gene over-expression, etc. Simultaneous integration of various datasets to perform system identification has the potential to avoid non-identifiability issues typically arising when only single datasets are used.

A skew-t variational Bayes filter (STVBF) is applied to indoor positioning with time-of-arrival (TOA) based distance measurements and pedestrian dead reckoning (PDR). The proposed filter accommodates large positive outliers caused by occasional non-line-of-sight (NLOS) conditions by using a skew-t model of measurement errors. Real-data tests using the fusion of inertial sensors based PDR and ultra-wideband based TOA ranging show that the STVBF clearly outperforms the extended Kalman filter (EKF) in positioning accuracy with the computational complexity about three times that of the EKF.

Gaussian process regression is a popular method for non-parametric probabilistic modeling of functions. The Gaussian process prior is characterized by so-called hyperparameters, which often have a large influence on the posterior model and can be difficult to tune. This work provides a method for numerical marginalization of the hyperparameters, relying on the rigorous framework of sequential Monte Carlo. Our method is well suited for online problems, and we demonstrate its ability to handle real-world problems with several dimensions and compare it to other marginalization methods. We also conclude that our proposed method is a competitive alternative to the commonly used point estimates maximizing the likelihood, both in terms of computational load and its ability to handle multimodal posteriors.

In order to investigate the cases in which an externally positive discrete-time system fails to have a minimal positive realization, in this paper we introduce the notion of minimal eventually positive realization, fr which the state update matrix becomes positive after a certain power. This property captures the idea that in the impulse response of an externally positive system the state of a minimal realization may fail to be positive, but only transiently. It is shown in the paper that whenever a minimal eventually positive realization exists, then the sequence of Markov parameters of the impulse response admits decimated subsequences for which minimal positive realizations exist and can be obtained by downsampling the eventually positive realization.

In Model Predictive Control (MPC) the control signal is computed by solving a constrained finite-time optimal control (CFTOC) problem at each sample in the control loop. The CFTOC problem can be solved by, e.g., interior-point or active-set methods, where the main computational effort in both methods is known to be the computation of the search direction, i.e., the Newton step. This is often done using generic sparsity exploiting algorithms or serial Riccati recursions, but as parallel hardware is becoming more commonly available the need for parallel algorithms for computing the Newton step is increasing. In this paper a tailored, non-iterative parallel algorithm for computing the Newton step using the Riccati recursion is presented. The algorithm exploits the special structure of the Karush-Kuhn-Tucker system for a CFTOC problem. As a result it is possible to obtain logarithmic complexity growth in the prediction horizon length, which can be used to reduce the computation time for popular state-of-the-art MPC algorithms when applied to what is today considered as challenging control problems.

One of the key challenges in identifying nonlinear and possibly non-Gaussian state space models (SSMs) is the intractability of estimating the system state. Sequential Monte Carlo (SMC) methods, such as the particle filter (introduced more than two decades ago), provide numerical solutions to the nonlinear state estimation problems arising in SSMs. When combined with additional identification techniques, these algorithms provide solid solutions to the nonlinear system identification problem. We describe two general strategies for creating such combinations and discuss why SMC is a natural tool for implementing these strategies.

Simultaneous localization and mapping (SLAM) is a well-known positioning approach in GPS-denied environments such as urban canyons and inside buildings. Autonomous/aided target detection and recognition (ATR) is commonly used in military application to detect threats and targets in outdoor environments. This papers present approaches to combine SLAM with ATR in ways that compensate for the drawbacks in each method. The methods use physical objects that are recognizable by ATR as unambiguous features in SLAM, while SLAM provides the ATR with better position estimates. Landmarks in the form of 3D point features based on normal aligned radial features (NARF) are used in conjunction with identified objects and 3D object models that replace landmarks when possible. This leads to a more compact map representation with fewer landmarks, which partly compensates for the introduced cost of the ATR. We analyze three approaches to combine SLAM and 3D-data; point-point matching ignoring NARF features, point-point matching using the set of points that are selected by NARF feature analysis, and matching of NARF features using nearest neighbor analysis. The first two approaches are is similar to the common iterative closest point (ICP). We propose an algorithm that combines EKF-SLAM and ATR based on rectangle estimation. The intended application is to improve the positioning of a first responder moving through an indoor environment, where the map offers localization and simultaneously helps locate people, furniture and potentially dangerous objects such as gas canisters.

We propose nested sequential Monte Carlo (NSMC), a methodology to sample from sequences of probability distributions, even where the random variables are high-dimensional. NSMC generalises the SMC framework by requiring only approximate, properly weighted, samples from the SMC proposal distribution, while still resulting in a correct SMC algorithm. Furthermore, NSMC can in itself be used to produce such properly weighted samples. Consequently, one NSMC sampler can be used to construct an efficient high-dimensional proposal distribution for another NSMC sampler, and this nesting of the algorithm can be done to an arbitrary degree. This allows us to consider complex and high-dimensional models using SMC. We show results that motivate the efficacy of our approach on several filtering problems with dimensions in the order of 100 to 1 000.

Data-efficient learning in continuous state-action spaces using very high-dimensional observations remains a key challenge in developing fully autonomous systems. In this paper, we consider one instance of this challenge, the pixels to torques problem, where an agent must learn a closed-loop control policy from pixel information only. We introduce a data-efficient, model-based reinforcement learning algorithm that learns such a closed-loop policy directly from pixel information. The key ingredient is a deep dynamical model that uses deep auto-encoders to learn a low-dimensional embedding of images jointly with a predictive model in this low-dimensional feature space. Joint learning ensures that not only static but also dynamic properties of the data are accounted for. This is crucial for long-term predictions, which lie at the core of the adaptive model predictive control strategy that we use for closed-loop control. Compared to state-of-the-art reinforcement learning methods for continuous states and actions, our approach learns quickly, scales to high-dimensional state spaces and is an important step toward fully autonomous learning from pixels to torques.

This paper concerns the estimation of approximate (linear) inverse models of block-oriented systems and the presented results give an improved understanding of these approximations. The estimated inverse is intended to be used as a pre- or postdistorter of the original system and a good inverse model would thus be one that, when used in series with the original system, produces a signal that resembles the original input. An inverse model of a nonlinear system can either be estimated in the standard way (from input to output) and then inverted, or directly (from output to input). This choice will affect the model. In the general case, the two modeling approaches will lead to different models, which will be shown for Hammerstein and Wiener systems. However, for a noise-free Hammerstein system with a white Gaussian input, the two approaches will result in the same model, up to a constant. When the two models are not equal, and the goal is to use the inverse as described above, it can be beneficial to estimate an approximate inverse directly. It will also be illustrated in an example how the inverse estimate can be used to get a nonparametric estimate of the nonlinearity in a block-oriented system.