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  • 1.
    Akin, Bilal
    et al.
    Texas Instruments Inc, USA.
    Choi, Seungdeog
    Texas A&M University, USA.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Toliyat, Hamid A
    Texas A&M University, USA.
    A Simple Real-Time Fault Signature Monitoring Tool for Motor-Drive-Embedded Fault Diagnosis Systems2011In: IEEE transactions on industrial electronics (1982. Print), ISSN 0278-0046, E-ISSN 1557-9948, Vol. 58, no 5, p. 1990-2001Article in journal (Refereed)
    Abstract [en]

    The reference frame theory constitutes an essential aspect of electric machine analysis and control. In this study, apart from the conventional applications, it is reported that the reference frame theory approach can successfully be applied to real-time fault diagnosis of electric machinery systems as a powerful toolbox to find the magnitude and phase quantities of fault signatures with good precision as well. The basic idea is to convert the associated fault signature to a dc quantity, followed by the computation of the signals average in the fault reference frame to filter out the rest of the signal harmonics, i.e., its ac components. As a natural consequence of this, neither a notch filter nor a low-pass filter is required to eliminate fundamental component or noise content. Since the incipient fault mechanisms have been studied for a long time, the motor fault signature frequencies and fault models are very well-known. Therefore, ignoring all other components, the proposed method focuses only on certain fault signatures in the current spectrum depending on the examined motor fault. Broken rotor bar and eccentricity faults are experimentally tested online using a TMS320F2812 digital signal processor (DSP) to prove the effectiveness of the proposed method. In this application, only the readily available drive hardware is used without employing additional components such as analog filters, signal conditioning board, external sensors, etc. As the motor drive processing unit, the DSP is utilized both for motor control and fault detection purposes, providing instantaneous fault information. The proposed algorithm processes the measured data in real time to avoid buffering and large-size memory needed in order to enhance the practicability of this method. Due to the short-time convergence capability of the algorithm, the fault status is updated in each second. The immunity of the algorithm against non-ideal cases such as measurement offset errors and phase unbalance is theoretically and experimentally verified. Being a model-independent fault analyzer, this method can be applied to all multiphase and single-phase motors.

  • 2.
    Ardeshiri, Tohid
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Middle East Technical University.
    Lundquist, Christian
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Schön, Thomas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    On mixture reduction for multiple target tracking2012Conference paper (Refereed)
  • 3.
    Ardeshiri, Tohid
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Özkan, Emre
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Middle East Technical University.
    On Reduction of Mixtures of the Exponential Family Distributions2013Report (Other academic)
    Abstract [en]

    Many estimation problems require a mixture reduction algorithm with which an increasing number of mixture components are reduced to a tractable level. In this technical report a discussion on dierent aspects of mixture reduction is given followed by a presentation of numerical simulation on reduction of mixture densities where the component density belongs to the exponential family of distributions.

  • 4.
    Ardeshiri, Tohid
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Özkan, Emre
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering. Linköping University, Faculty of Science & Engineering.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Variational Iterations for Smoothing with Unknown Process and Measurement Noise Covariances2015Report (Other academic)
    Abstract [en]

    In this technical report, some derivations for the smoother proposed in [1] are presented. More specifically, the derivations for the cyclic iteration needed to solve the variational Bayes smoother for linear state-space models with unknownprocess and measurement noise covariances in [1] are presented. Further, the variational iterations are compared with iterations of the Expectation Maximization (EM) algorithm for smoothing linear state-space models with unknown noise covariances.

    [1] T. Ardeshiri, E. Özkan, U. Orguner, and F. Gustafsson, ApproximateBayesian smoothing with unknown process and measurement noise covariances, submitted to Signal Processing Letters, 2015.

  • 5.
    Axelsson, Patrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Norrlöf, Mikael
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    ML Estimation of Process Noise Variance in Dynamic Systems2010Report (Other academic)
    Abstract [en]

    The performance of a non-linear filter hinges in the end on the accuracy of the assumed non-linear model of the process. In particular, the process noise covariance $Q$ is hard to get by physical modeling and dedicated system identification experiments. We propose a variant of the expectation maximization (EM) algorithm which iteratively estimates the unobserved state sequence and $Q$ based on the observations of the process. The extended Kalman smoother (EKS) is the instrument to find the unobserved state sequence. Our contribution fills a gap in literature, where previously only the linear Kalman smoother and particle smoother have been applied. The algorithm will be important for future industrial robots with more flexible structures, where the particle smoother cannot be applied due to the high state dimension. The proposed method is compared to two alternative methods on a simulated robot.

  • 6.
    Axelsson, Patrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Norrlöf, Mikael
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    ML Estimation of Process Noise Variance in Dynamic Systems2011In: Proceedings of the 18th IFAC World Congress, 2011, p. 5609-5614Conference paper (Refereed)
    Abstract [en]

    The performance of a non-linear filter hinges in the end on the accuracy of the assumed non-linear model of the process. In particular, the process noise covariance Q is hard to get by physical modeling and dedicated system identification experiments. We propose a variant of the expectation maximization (EM) algorithm which iteratively estimates the unobserved state sequence and Q based on the observations of the process. The extended Kalman smoother (EKS) is the instrument to find the unobserved state sequence. Our contribution fills a gap in literature, where previously only the linear Kalman smoother and particle smoother have been applied. The algorithm will be important for future industrial robots with more flexible structures, where the particle smoother cannot be applied due to the high state dimension. The proposed method is compared to two alternative methods on a simulated robot.

  • 7.
    Bshara, M.
    et al.
    Higher Institute for Applied Sciences and Technology, Damascus, Syrian Arab Republic.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    L. Van, Biesen
    Universiteit Brussel, Brussels, Belgium.
    GPS positioning and groung-truth reference points generation2011In: Joint IMEKO TC11-TC19-TC20 Int. Symp. Metrological Infrastructure, Environmental and Energy Measurement and Int. Symp. of Energy Agencies of Mediterranean Countries, IMEKO-MI 2011, Curran Associates, Incorporated, 2011 , 2011, p. 111-116Conference paper (Refereed)
    Abstract [en]

    The global positioning system (GPS) is a Global Navigation Satellite System (GNSS) uses a constellation of between 24 and 32 Medium Earth Orbit satellites that transmit precise microwave signals, which enable GPS receivers to determine their current location, the time, and their velocity [1]. Initially, the GPS was developed for military applications, but very quickly became the most used technology in positioning even for end-user applications run by individuals with no technical skills. GPS reading are used also as reference points for many positioning techniques such as the techniques that depend on the transmitted electromagnetic signal to determine the position of the transmitter or the receiver, due to their superior accuracy comparing to such techniques. But how accurate are those readings, and how to obtain accurate reference points starting from raw GPS observations even when they are corrupted with errors. In this paper, a practical study about GPS positioning is provided. Generating the ground-truth reference points depending on GPS observations is also provided and discussed in details.

  • 8.
    Bshara, Mussa
    et al.
    Vrije University Brussels, Belgium.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Van Biesen, Leo
    Vrije University Brussels, Belgium.
    Fingerprinting Localization in Wireless Networks Based on Received-Signal-Strength Measurements: A Case Study on WiMAX Networks2010In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 59, no 1, p. 283-294Article in journal (Refereed)
    Abstract [en]

    This paper considers the problem of fingerprinting localization in wireless networks based on received-signal-strength (RSS) observations. First, the performance of static localization using power maps (PMs) is improved with a new approach called the base-station-strict (BS-strict) methodology, which emphasizes the effect of BS identities in the classical fingerprinting. Second, dynamic motion models with and without road network information are used to further improve the accuracy via particle filters. The likelihood-calculation mechanism proposed for the particle filters is interpreted as a soft version (called BS-soft) of the BS-strict approach applied in the static case. The results of the proposed approaches are illustrated and compared with an example whose data were collected from a WiMAX network in a challenging urban area in the capitol city of Brussels, Belgium.

  • 9.
    Bshara, Mussa
    et al.
    Vrije Universiteit Brussel, Belgium.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Van Biesen, Leo
    Vrije Universiteit Brussel, Belgium.
    Robust Tracking in Cellular Networks Using HMM Filters and Cell-ID Measurements2011In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 60, no 3, p. 1016-1024Article in journal (Refereed)
    Abstract [en]

    A localization algorithm based on cell identification (Cell-ID) information is proposed. Instead of building the localization decisions only on the serving base station, all the detected Cell-IDs (serving or nonserving) by the mobile station are utilized. The statistical modeling of user motion and the measurements are done via a hidden Markov model (HMM), and the localization decisions are made with maximum a posteriori estimation criterion using the posterior probabilities from an HMM filter. The results are observed and compared with standard alternatives on an example whose data were collected from a worldwide interoperability for microwave access network in a challenging urban area in the Brussels capitol city.

  • 10.
    Burak Guldogan, Mehmet
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gaussian mixture PHD filter for multi-target tracking using passive doppler-only measurements2012In: IET Conference Publications: vol 2012, issue 595 CP, IEEE conference proceedings, 2012, Vol. 2012, no 595 CP, p. 1-6Conference paper (Refereed)
    Abstract [en]

    In this paper, we analyze the performance of the Gaussian mixture probability hypothesis density (GM-PHD) filter in tracking multiple non-cooperative targets using a passive sensor network. Non-cooperative transmissions from illuminators of opportunity like GSM base stations, FM radio transmitters or digital broadcasters are exploited by non-directional separately located Doppler measuring sensors. Clutter, missed detections and multi-static Doppler variances are incorporated into a realistic multi-target scenario. Simulation results show that the GM-PHD filter successfully tracks multiple targets using only Doppler shift measurements in a passive multi-static scenario.

  • 11.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Lundquist, Christian
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    A Gaussian Mixture PHD Filter for Extended Target Tracking2010In: Proceedings of the 13th International Conference on Information Fusion, 2010Conference paper (Refereed)
    Abstract [en]

    In extended target tracking, targets potentially produce more than one measurement per time step. Multiple extended targets are therefore usually hard to track, due to the resulting complex data association. The main contribution of this paper is the implementation of a Probability Hypothesis Density (PHD) filter for tracking of multiple extended targets. A general modification of the PHD filter to handle extended targets has been presented recently by Mahler, and the novelty in this work lies in the realisation of a Gaussian mixture PHD filter for extended targets. Furthermore, we propose a method to easily partition the measurements into a number of subsets, each of which is supposed to contain measurements that all stem from the same source. The method is illustrated in simulation examples, and the advantage of the implemented extended target PHD filter is shown in a comparison with a standard PHD filter.

  • 12.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Lundquist, Christian
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Extended Target Tracking Using a Gaussian-Mixture PHD Filter2012In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 48, no 4, p. 3268-3286Article in journal (Refereed)
    Abstract [en]

    This paper presents a Gaussian-mixture implementation of the phd filter for tracking extended targets. The exact filter requires processing of all possible measurement set partitions, which is generally infeasible to implement. A method is proposed for limiting the number of considered partitions and possible alternatives are discussed. The implementation is used on simulated data and in experiments with real laser data, and the advantage of the filter is illustrated. Suitable remedies are given to handle spatially close targets and target occlusion.

  • 13.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Lundquist, Christian
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Tracking Rectangular and Elliptical Extended Targets Using Laser Measurements2011In: Proceedings of the 14th International Conference on Information Fusion, 2011, p. 592-599Conference paper (Refereed)
    Abstract [en]

    This paper considers tracking of extended targets using data from laser range sensors. Two types of extended target shapes are considered, rectangular and elliptical, and a method to compute predicted measurements and corresponding innovation covariances is suggested. The proposed method can easily be integrated into any tracking framework that relies on the use of an extended Kalman filter. Here, it is used together with a recently proposed Gaussian mixture probability hypothesis density (GM-PHD) filter for extended target tracking, which enables estimation of not only position, orientation, and size of the extended targets, but also estimation of extended target type (i.e. rectangular or elliptical). In both simulations and experiments using laser data, the versatility of the proposed tracking framework is shown. In addition, a simple measure to evaluate the extended target tracking results is suggested.

  • 14.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Department of Electrical and Electronics Engineering, Middle East Technical University.
    A PHD Filter for Tracking Multiple Extended Targets using Random Matrices2012In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 11, p. 5657-5671Article in journal (Refereed)
    Abstract [en]

    This paper presents a random set based approach to tracking of an unknown number of extended targets, in the presence of clutter measurements and missed detections, where the targets extensions are modeled as random matrices. For this purpose, the random matrix framework developed recently by Koch et al. is adapted into the extended target PHD framework, resulting in the Gaussian inverse Wishart PHD (GIW-PHD) filter. A suitable multiple target likelihood is derived, and the main filter recursion is presented along with the necessary assumptions and approximations. The particularly challenging case of close extended targets is addressed with practical measurement clustering algorithms. The capabilities and limitations of the resulting extended target tracking framework are illustrated both in simulations and in experiments based on laser scans.

  • 15.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    On Spawning and Combination of Extended/Group Targets Modeled with Random Matrices2013In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 61, no 3, p. 678-692Article in journal (Refereed)
    Abstract [en]

    In extended/group target tracking, where the extensions of the targets are estimated, target spawning and combination events might have significant implications on the extensions. This paper investigates target spawning and combination events for the case that the target extensions are modeled in a random matrix framework. The paper proposes functions that should be provided by the tracking filter in such a scenario. The results, which are obtained by a gamma Gaussian inverse Wishart implementation of an extended target probability hypothesis density filter, confirms that the proposed functions improve the performance of the tracking filter for spawning and combination events.

  • 16.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Properties and Approximations of some Matrix Variate Probability Density Functions2011Report (Other academic)
    Abstract [en]

    This report contains properties and approximations of some matrix valued probability density functions. Expected values of functions of generalised Beta type II distributed random variables are derived. In two Theorems, approximations of matrix variate distributions are derived. A third theorem contain a marginalisation result.

  • 17.
    Guldogan, Mehmet Burak
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Björklund, Svante
    Swedish Defence Research Agency, Linköping, Sweden.
    Petersson, H.
    Swedish Defence Research Agency, Linköping, Sweden.
    Nezirovic, A.
    Swedish Defence Research Agency, Linköping, Sweden.
    Human gait parameter estimation based on micro-doppler signatures using particle filters2011In: Acoustics, Speech and Signal Processing (ICASSP), 2011, IEEE , 2011, p. 5940-5943Conference paper (Refereed)
    Abstract [en]

    Monitoring and tracking human activities around restricted areas is an important issue in security and surveillance applications. The movement of different parts of the human body generates unique micro-Doppler features which can be extracted effectively using joint time-frequency analysis. In this paper, we describe the simultaneous tracking of both location and micro-Doppler features of a human using particle filters (PF). The results obtained using the data from a 77 GHz radar prove the successful usage of particle filters in tracking micro-Doppler features of the human gait.

  • 18.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Schön, Thomas B.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Skoglar, Per
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Karlsson, G Rickard
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Navigation and Tracking of Road-Bound Vehicles2012In: Handbook of Intelligent Vehicles / [ed] Eskandarian, Azim, London: Springer, 2012, p. 397-434Chapter in book (Refereed)
    Abstract [en]

    The Handbook of Intelligent Vehicles provides a complete coverage of the fundamentals, new technologies, and sub-areas essential to the development of intelligent vehicles; it also includes advances made to date, challenges, and future trends. Significant strides in the field have been made to date; however, so far there has been no single book or volume which captures these advances in a comprehensive format, addressing all essential components and subspecialties of intelligent vehicles, as this book does. Since the intended users are engineering practitioners, as well as researchers and graduate students, the book chapters do not only cover fundamentals, methods, and algorithms but also include how software/hardware are implemented, and demonstrate the advances along with their present challenges. Research at both component and systems levels are required to advance the functionality of intelligent vehicles. This volume covers both of these aspects in addition to the fundamentals listed above. 

  • 19.
    Lundquist, Christian
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Granström, Karl
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Estimating the Shape of Targets with a PHD Filter2011In: Proceedings of the 14th International Conference on Information Fusion, 2011Conference paper (Refereed)
    Abstract [en]

    This paper presents a framework for tracking extended targets which give rise to a structured set of measurements per each scan. The concept of a measurement generating point (MGP) which is defined on the boundary of each target is introduced. The tracking framework contains an hybrid statespace where MGP:s and the measurements are modeled by random finite sets and target states by random vectors. The target states are assumed to be partitioned into linear and nonlinear components and a Rao-Blackwellized particle filter is used for their estimation. For each state particle, a probability hypothesis density (PHD) filter is utilized for estimating the conditional set of MGP:s given the target states. The PHD kept for each particle serves as a useful means to represent information in the set of measurements about the target states. The early results obtained show promising performance with stable target following capability and reasonable shape estimates.

  • 20.
    Lundquist, Christian
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Estimating Polynomial Structures from Radar Data2010In: Proceedings of the 13th International Conference on Information Fusion, Edinburgh, Scotland, 2010Conference paper (Refereed)
    Abstract [en]

    Situation awareness for vehicular safety and autonomy functions includes knowledge of the drivable area. This area is normally constrained between stationary road-side objects as guard-rails, curbs, ditches and vegetation. We consider these as extended objects modeled by polynomials along the road, and propose an algorithm to track each polynomial based on noisy range and bearing detections, typically from a radar. A straightforward Kalman filter formulation of the problem suffers from the errors-in-variables (EIV) problem in that the noise enters the system model. We propose an EIV modification of the Kalman filter and demonstrates its usefulness using radar data from public roads.

  • 21.
    Lundquist, Christian
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Extended Target Tracking Using Polynomials With Applications to Road-Map Estimation2011In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 59, no 1, p. 15-26Article in journal (Refereed)
    Abstract [en]

    This paper presents an extended target tracking framework which uses polynomials in order to model extended objects in the scene of interest from imagery sensor data. State-space models are proposed for the extended objects which enables the use of Kalman filters in tracking. Different methodologies of designing measurement equations are investigated. A general target tracking algorithm that utilizes a specific data association method for the extended targets is presented. The overall algorithm must always use some form of prior information in order to detect and initialize extended tracks from the point tracks in the scene. This aspect of the problem is illustrated on a real life example of road-map estimation from automotive radar reports along with the results of the study.

  • 22.
    Lundquist, Christian
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Schön, Thomas B.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Estimation of the Free Space in Front of a Moving Vehicle2009Report (Other academic)
    Abstract [en]

    There are more and more systems emerging making use of measurements from a forward looking radar and a forward looking camera. It is by now well known how to exploit this data in order to compute estimates of the road geometry, tracking leading vehicles, etc. However, there is valuable information present in the radar concerning stationary targets, that is typically not used. The present work shows how radar measurements of stationary targets can be used to provide a reliable estimate of the drivable space in front of a moving vehicle.

    In the present paper three conceptually different methods to estimate stationary objects or road borders are presented and compared. The first method considered is occupancy grid mapping, which discretizes the map surrounding the ego vehicle and the probability of occupancy is estimated for each grid cell. The second method applies a constrained quadratic program in order to estimate the road borders. The problem is stated as a constrained curve fitting problem. The third method associates the radar measurements to extended stationary objects and tracks them as extended targets.

    The required sensors, besides the standard proprioceptive sensors of a modern car, are a forward looking long range radar and a forward looking camera. Hence, there is no need to introduce any new sensors, it is just a matter of making better use of the sensor information that is already present in a modern car. The approach has been evaluated and tested on real data from highways and rural roads in Sweden and the results are very promising.

  • 23.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    A Variational Measurement Update for Extended Target Tracking With Random Matrices2012In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 7, p. 3827-3834Article in journal (Refereed)
    Abstract [en]

    This correspondence proposes a new measurement update for extended target tracking under measurement noise when the target extent is modeled by random matrices. Compared to the previous measurement update developed by Feldmann et al., this work follows a more rigorous path to derive an approximate measurement update using the analytical techniques of variational Bayesian inference. The resulting measurement update, though computationally more expensive, is shown via simulations to be better than the earlier method in terms of both the state estimates and the predictive likelihood for moderate amounts of prediction errors.

  • 24.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Entropy Calculation in Particle Filters2009In: Proceedings of the 17th IEEE Signal Processing and Communications Applications Conference, 2009, p. 628-631Conference paper (Refereed)
    Abstract [en]

    This paper presents a differential entropy calculation method to be used for particle mixtures in particle filters. First it is shown that the exact differential entropy of particle mixtures is minus infinity and therefore useless in practice. The disadvantage of using discrete entropy formulation instead of differential entropy is also explained. Unlike the kernel-based methods in the literature, a Bayes rule based approximation is then proposed. The performance of the algorithm is illustrated on a basic Gaussian mixture example.

  • 25.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Notes on Differential Entropy Calculation Using Particles2008Report (Other academic)
    Abstract [en]

    This report outlines a method to calculate the differential entropy of a probability density represented by a number of particles and weights. When only the particles and the weights are given, entropy calculation is cumbersome and requires continuous approximation of the density by using some kernel functions. However, in a particle ltering framework, Bayes rule provides a direct sample based approximation to the problem.

  • 26.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Notes on Differential Entropy of Mixtures2008Report (Other academic)
    Abstract [en]

    This report proves that the differential entropy of particle mixtures is equal to -∞ unlike the wrong claim in the literature that is equal to the discrete entropy of particle weights. It then gives an upper bound for the differential entropy of the Gaussian mixtures which can be used in practical applications.

  • 27.
    Orguner, Umut
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Demirekler, Mübeccel
    Middle East Technical University, Turkey.
    Maximum Likelihood Estimation of Transition Probabilities of Jump Markov Linear Systems2008In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 56, no 10 II, p. 5093-5108Article in journal (Refereed)
    Abstract [en]

    This paper describes an online maximum likelihood estimator for the transition probabilities associated with a jump Markov linear system (JMLS). The maximum likelihood estimator is derived using the reference probability method, which exploits an hypothetical probability measure to find recursions for complex expectations. Expectation maximization (EM) procedure is utilized for maximizing the likelihood function. In order to avoid the exponential increase in the number of statistics of the optimal EM algorithm, we make interacting multiple model (IMM)-type approximations. The resulting method needs the mode weights of an IMM filter with N3 components, where N is the number of models in the JMLS. The algorithm can also supply base-state estimates and covariances as a by-product. The performance of the estimator is illustrated on two simulated examples and compared to a recently proposed alternative.

  • 28.
    Orguner, Umut
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Multi Target Tracking with Acoustic Power Measurements using Emitted Power Density2010In: Proceedings of the 13th International Conference on Information Fusion, 2010Conference paper (Refereed)
    Abstract [en]

    This paper presents a method to achieve multi target tracking using acoustic power measurements obtained from an acoustic sensor network. We first present a novel concept called emitted power density (EPD) which is an aggregate information state that holds the emitted power distribution of all targets in the scene over the target state space. It is possible to find prediction and measurement update formulas for an EPD which is conceptually similar to a probability hypothesis density (PHD). We propose a Gaussian process based representation for making the related EPD updates using Kalman filter formulas. These updates constitute a recursive EPD-filter which is based on the discretization of the position component of the target state space. The results are illustrated on a real data scenario where experiments are done with two targets constrained to a road segment.

  • 29.
    Orguner, Umut
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Particle Filtering with Propagation Delayed Measurements2010In: Proceedings of the 2010 IEEE Aerospace Conference, 2010, p. 1-9Conference paper (Refereed)
    Abstract [en]

    This paper investigates the problem of propagation delayed measurements in a particle filtering scenario. Based on implicit constraints specified by target dynamics and physics rules of signal propagation, authors apply the ideas that were first proposed in their previous work to the case of particle filters. Unlike the deterministic sampling based approach called propagation delayed measurement filter (PDMF) in their previous work, the new algorithm proposed here (called as PDM particle filter (PDM-PF)) has the potential to be used with general nonlinear models. This advantage and the estimation results of PDM-PF are illustrated in a challenging target tracking scenario by making comparisons to PDMF along with some other alternative particle filters.

  • 30.
    Orguner, Umut
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Risk Sensitive Particle Filters for Mitigating Sample Impoverishment2007In: Proceedings of the 14th IEEE/SP Workshop on Statistical Signal Processing, 2007, , p. 259-263p. 259-263Conference paper (Refereed)
    Abstract [en]

    Risk-sensitive filters (RSF) put a penalty to higher-order moments of the estimation error compared to conventional filters as the Kalman filter minimizing the mean square error. The result is a more cautious filter, which can be interpreted as an implicit and automatic way to increase the state noise covariance. On the other hand, the process of jittering, or roughening, is well-known in particle filters to mitigate sample impoverishment. The purpose of this contribution is to introduce risk-sensitive particle filters (RSPF) as an alternative approach to mitigate sample impoverishment based on constructing explicit risk functions from a general class of factorizable functions.

  • 31.
    Orguner, Umut
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Risk-Sensitive Particle Filters for Mitigating Sample Impoverishment2008In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 56, no 10 II, p. 5001-5012Article in journal (Refereed)
    Abstract [en]

    Risk-sensitive filters (RSF) put a penalty to higher-order moments of the estimation error compared to conventional filters as the Kalman filter minimizing the mean square error (MSE). The result is a more cautious filter, which can be interpreted as an implicit and automatic way to increase the state noise covariance. On the other hand, the process of jittering, or roughening, is well known in particle filters to mitigate sample impoverishment. The purpose of this contribution is to introduce risk-sensitive particle filters (RSPF) as an alternative approach to mitigate sample impoverishment based on constructing explicit risk functions from a general class of factorizable functions. It is first shown that RSF can be done in nonlinear systems using a recursion of an infinite dimensional information state which involves general risk functions. Then, this information state calculation is carried out using particle approximations. Some alternative approaches, generalizations, specific cases, comparison to existing methods of sample impoverishment mitigation and issues related to the selection of risk functions and parameters are examined. Performance of the resulting filter using various risk functions is illustrated on a simulated scenario and compared with the roughening method.

  • 32.
    Orguner, Umut
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Statistical Characteristics of Harris Corner Detector2007In: Proceedings of the 14th IEEE/SP Workshop on Statistical Signal Processing, 2007, , p. 571-575p. 571-575Conference paper (Refereed)
    Abstract [en]

    In this study, a method is proposed to calculate the bias and mean square error matrix of Harris detector calculated corners. The main result is presented in a theorem and the performance of the algorithm is shown on an example.

  • 33.
    Orguner, Umut
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Storage Efficient Particle Filters for the out of Sequence Measurement Problem2008In: Proceedings of the 11th International Conference on Information Fusion, 2008Conference paper (Refereed)
    Abstract [en]

    We propose a particle filter based solution which uses auxiliary fixed point smoothers to the problem of out of sequence measurements. Three different cases, namely, auxiliary extended Kalman smoother, auxiliary unscented Kalman smoother and auxiliary particle smoother are considered for the auxiliary fixed point smoother. The proposed filter which can effectively combine out of sequence measurements with arbitrary delay uses minimum storage requirements as opposed to a recently proposed alternative. The performance of our filters is compared to the other alternative in the literature using a simulated scenario of a target with a highly nonlinear motion dynamics and measurement relations. For this purpose, an extension of the alternative method is also proposed enabling it to work with nonlinear state dynamics.

  • 34.
    Orguner, Umut
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Target Tracking using Delayed Measurements with Implicit Constraints2008In: Proceedings of the 11th International Conference on Information Fusion, 2008, p. 1-8Conference paper (Refereed)
    Abstract [en]

    In target tracking, standard sensors as radar and EO/IR observe the target with a negligible delay, since the speed of light is much larger than the speed of the target. This contribution studies the case where the ratio of the target and the propagation speed is not negligible, as is the case in sensor networks with microphones, geophones or sonars for instance, where the speed of air, ground waves and water cause a state dependent and stochastic delay of the observations. The proposed approach utilizes a temporary augmentation of the state vector with the observation delay, and sampling based approaches are suggested for modifying standard filters as the extended Kalman filter, unscented Kalman filters and possibly particle filters. An application example with bearing only tracking using a single microphone array is used as an illustration.

  • 35.
    Orguner, Umut
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Target Tracking With Particle Filters Under Signal Propagation Delays2011In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 59, no 6, p. 2485-2495Article in journal (Refereed)
    Abstract [en]

    Signal propagation delays are hardly a problem for target tracking with standard sensors such as radar and vision due to the fact that the speed of light is much higher than the speed of the target. This contribution studies the case where the ratio of the target and the propagation speed is not negligible, as in the case of sensor networks with microphones, geophones or sonars for instance, where the signal speed in air, ground and water causes a state dependent and stochastic delay of the observations. The proposed approach utilizes an augmentation of the state vector with the propagation delay in a particle filtering framework to compensate for the negative effects of the delays. The model of the physics rules governing the propagation delays is used in interaction with the target motion model to yield an iterative prediction update step in the particle filter which is called the propagation delayed measurement particle filter (PDM-PF). The performance of PDM-PF is illustrated in a challenging target tracking scenario by making comparisons to alternative particle filters that can be used in similar cases.

  • 36.
    Orguner, Umut
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Lundquist, Christian
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Granström, Karl
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Extended Target Tracking with a Cardinalized Probability Hypothesis Density Filter2011In: Proceedings of 2011 International Conference on Information Fusion (FUSION), 2011Conference paper (Refereed)
    Abstract [en]

    This paper presents a cardinalized probability hypothesis density (CPHD) filter for extended targets that can result in multiple measurements at each scan. The probability hypothesis density (PHD) filter for such targets has already been derived by Mahler and a Gaussian mixture implementation has been proposed recently. This work relaxes the Poisson assumptions of the extended target PHD filter in target and measurement numbers to achieve better estimation performance. A Gaussian mixture implementation is described. The early results using real data from a laser sensor confirm that the sensitivity of the number of targets in the extended target PHD filter can be avoided with the added flexibility of the extended target CPHD filter.

  • 37.
    Orguner, Umut
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Schön, Thomas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Improved Target Tracking with Road Network Information2009Report (Other academic)
    Abstract [en]

    In this paper we consider the problem of tracking targets, which can move both on-road and off-road, with particle filters utilizing the road-network information. It is argued that the constraints like speed-limits and/or one-way roads generally incorporated into on-road motion models make it necessary to consider additional high-bandwidth off-road motion models. This is true even if the targets under consideration are only allowed to move on-road due to the possibility of imperfect road-map information and drivers violating the traffic rules. The particle filters currently used struggles during sharp mode transitions, with poor estimation quality as a result. This is due to the fact the number of particles allocated to each motion mode is varying according to the mode probabilities. A recently proposed interacting multiple model (IMM) particle filtering algorithm, which keeps the number of particles in each mode constant irrespective of the mode probabilities, is applied to this problem and its performance is compared to a previously existing algorithm. The results of the simulations on a challenging bearing-only tracking scenario show that the proposed algorithm, unlike the previously existing algorithm, can achieve good performance even under the sharpest mode transitions.

  • 38.
    Orguner, Umut
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Schön, Thomas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Improved Target Tracking with Road Network Information2009In: Proceedings of the '09 IEEE Aerospace Conference, 2009, p. 1-11Conference paper (Refereed)
    Abstract [en]

    In this paper we consider the problem of tracking targets, which can move both on-road and off-road, with particle filters utilizing the road-network information. It is argued that the constraints like speed-limits and/or one-way roads generally incorporated into on-road motion models make it necessary to consider additional high-bandwidth off-road motion models. This is true even if the targets under consideration are only allowed to move on-road due to the possibility of imperfect road-map information and drivers violating the traffic rules. The particle filters currently used struggles during sharp mode transitions, with poor estimation quality as a result. This is due to the fact the number of particles allocated to each motion mode is varying according to the mode probabilities. A recently proposed interacting multiple model (IMM) particle filtering algorithm, which keeps the number of particles in each mode constant irrespective of the mode probabilities, is applied to this problem and its performance is compared to a previously existing algorithm. The results of the simulations on a challenging bearing-only tracking scenario show that the proposed algorithm, unlike the previously existing algorithm, can achieve good performance even under the sharpest mode transitions.

  • 39.
    Orguner, Umut
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Skoglar, Per
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Törnqvist, David
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Combined Point Mass and Particle Filter for Target Tracking2010In: Proceedings of the 2010 IEEE Aerospace Conference, 2010Conference paper (Refereed)
    Abstract [en]

    This paper presents a combined Point Mass Filter (PMF) and Particle Filter (PF), which utilizes the support of the PMF and the high particle density in the PF close to the current estimate. The result is a filter robust to unexpected process events but still with low error covariance. This filter is especially useful for target tracking applications, where target maneuvers suddenly can change unpredictably.

  • 40.
    Roth, Michael
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Dept. of Electrical and Electronics Engineering, Middle East Technical University Ankara, Turkey.
    On-road Trajectory Generation from GPS Data: A Particle Filtering/Smoothing Application2012In: 2012 15th International Conference on Information Fusion, IEEE , 2012, p. 779-786Conference paper (Refereed)
    Abstract [en]

    Many studies in target localization and tracking use GPS measurements as ground truth. These GPS locations might be in conflict with computed estimates in applications where road network information is available (and employed in the estimation procedure). This paper proposes to use particle methods to generate on-road trajectories that can be used as improved ground truth for such road constrained estimation schemes. A bootstrap particle filter and three different particle smoothers are utilized to obtain kinematic target state estimates. The particle smoothers require important adjustments for their implementation in the resulting hybrid state space. The performances of the presented methods are compared on challenging real data obtained from an urban area.

    Although particle filters and smoothers can be applied to general localization problems, with arbitrary sensors, we concentrate on GPS measurements, motivated by an application in cellular network systems.

  • 41.
    Saha, Saikat
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Nonlinear Filtering based on Observations from Student's T Processes2012In: Proceedings of the 2012 IEEE Aerospace Conference, 2012, , p. 6Conference paper (Refereed)
    Abstract [en]

    We consider measurements from possibly zero-mean stochastic processes in a nonlinear filtering framework. This is a challenging problem, since it is only the second order properties of the measurements that bear information about the unknown state vector. The covariance function of the measurements can have both spatial and temporal correlation that depend on the state. Recently, a solution to this problem was presented for the case of Gaussian processes. We here extend the theory to Student's t processes. We illustrate the state observability by a simple but still realistic simulation example.

  • 42.
    Skoglar, Per
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    On Information Measures for Bearings-only Estimation of a Random Walk Target2009Report (Other academic)
    Abstract [en]

    This report considers the bearings-only estimation problem of a random walk target. The estimation performance for a number of information measures in the Extended Kalman filter framework are investigated, both from a theoretical point of view and by simulation examples.

  • 43.
    Skoglar, Per
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    On Information Measures based on Particle Mixture for Optimal Bearings-only Tracking2009In: Proceedings of the 2009 IEEE Aerospace Conference, IEEE conference proceedings, 2009, p. 1-14Conference paper (Refereed)
    Abstract [en]

    In this work we consider a target tracking scenario where a moving observer with a bearings-only sensor is tracking a target. The tracking performance is highly dependent on the trajectory of the sensor platform, and the problem is to determine how it should maneuver for optimal tracking performance. The problem is considered as a stochastic optimal control problem and two sub-optimal control strategies are presented based on the Information filter and the determinant of the information matrix as the optimization objective. Using the determinant of the information matrix as an objective function in the planning problem is equivalent to using differential entropy of the posterior target density when it is Gaussian. For the non-Gaussian case, an approximation of the differential entropy of a density represented by a particle mixture is proposed. Furthermore, a gradient approximation of the differential entropy is derived and used in a stochastic gradient search algorithm applied to the planning problem.

  • 44.
    Skoglar, Per
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    On Information Measures based on Particle Mixture for Optimal Bearings-only Tracking2009Report (Other academic)
    Abstract [en]

    In this work we consider a target tracking scenario where a moving observer with a bearings-only sensor is tracking a target. The tracking performance is highly dependent on the trajectory of the sensor platform, and the problem is to determine how it should maneuver for optimal tracking performance.The problem is considered as a stochastic optimal control problem and two sub-optimal control strategies are presented based on the Information filter and the determinant of the information matrix as the optimization objective. Using the determinant of the information matrix as an objective function in the planning problem is equivalent to using differential entropy of the posterior target density when it is Gaussian. For the non-Gaussian case, an approximation of the differential entropy of a density represented by a particle mixture is proposed. Furthermore, a gradient approximation of the differential entropy is derived and used in a stochastic gradient search algorithm applied to the planning problem.

  • 45.
    Skoglar, Per
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Törnqvist, David
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Pedestrian Tracking with an Infrared Sensor using Road Network Information2012In: EURASIP Journal on Advances in Signal Processing, ISSN 1687-6172, E-ISSN 1687-6180, Vol. 1, no 26, p. 2012a-Article in journal (Refereed)
    Abstract [en]

    This article presents a pedestrian tracking methodology using an infrared sensor for surveillance applications. A distinctive feature of this study compared to the existing pedestrian tracking approaches is that the road network information is utilized for performance enhancement. A multiple model particle filter, which uses two different motion models, is designed for enabling the tracking of both road-constrained (on-road) and unconstrained (off-road) targets. The lateral position of the pedestrians on the walkways are taken into account by a specific on-road target model. The overall framework seamlessly integrates the negative information of occlusion events into the algorithm for which the required modifications are discussed. The resulting algorithm is illustrated on real data from a field trial for different scenarios.

  • 46.
    Skoglar, Per
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Törnqvist, David
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Road Target Search and Tracking with Gimballed Vision Sensor on a UAV2012Report (Other academic)
    Abstract [en]

    This work considers a sensor management problem where a number of road bounded vehicles are monitored by a UAV with a gimballed vision sensor. The problem is to keep track of all discovered targets and simultaneously search for new targets by controlling the pointing direction of the vision sensor and the motion of the UAV. A planner based on a state-machine is proposed with three different modes; target tracking, known target search, and new target search. A high-level decision maker chooses among these sub-tasks to obtain an overall situational awareness. A utility measure for evaluating the combined search and target tracking performance is also proposed. By using this measure it is possible to evaluate and compare the rewards of updating known targets versus searching for new targets in the same framework. The targets are assumed to be road bounded and the road network information is used both to improve the tracking and sensor management performance. The tracking and search are based on flexible target density representations provided by particle mixtures and deterministic grids.

  • 47.
    Skoglar, Per
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Törnqvist, David
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Road Target Search and Tracking with Gimballed Vision Sensor on an Unmanned Aerial Vehicle2012In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 4, no 7, p. 2076-2111Article in journal (Refereed)
    Abstract [en]

    This article considers a sensor management problem where a number of road bounded vehicles are monitored by an unmanned aerial vehicle (UAV) with a gimballed vision sensor. The problem is to keep track of all discovered targets and simultaneously search for new targets by controlling the pointing direction of the vision sensor and the motion of the UAV. A planner based on a state-machine is proposed with three different modes; target tracking, known target search, and new target search. A high-level decision maker chooses among these sub-tasks to obtain an overall situational awareness. A utility measure for evaluating the combined search and target tracking performance is also proposed. By using this measure it is possible to evaluate and compare the rewards of updating known targets versus searching for new targets in the same framework. The targets are assumed to be road bounded and the road network information is used both to improve the tracking and sensor management performance. The tracking and search are based on flexible target density representations provided by particle mixtures and deterministic grids.

  • 48.
    Skoglar, Per
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Törnqvist, David
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Road Target Tracking with an Approximative Rao-Blackwellized Particle Filter2009In: Proceedings from the 12th International Conference on Information Fusion, 6-9 July, Seattle, Washington, USA, IEEE conference proceedings, 2009, p. 17-24Conference paper (Refereed)
    Abstract [en]

    Using prior information about the road network will improve the estimation performance for a road constrained target significantly. Several estimation methods have been proposed to handle the multi-modality that arise in a road target tracking application. One popular filter suitable for this kind of non-linear problems is the Particle Filter, but a major drawback is that the Particle filter requires a large amount of particles as the state dimension increases to maintain a good approximation of the filtering distribution. In this paper a Rao-Blackwellized Particle Filter based approach is proposed to reduce the dimension of the state space in road target tracking applications. Furthermore, it is also shown how prior information about the probability of detection can be used to improve the estimation performance further.

1 - 48 of 48
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