Group and extended target tracking with the Probability Hypothesis Density filter

Engineering and Physical Sciences Research Council (EPSRC) Industrial CASE Award Studentship

[1]  R. Redner,et al.  Mixture densities, maximum likelihood, and the EM algorithm , 1984 .

[2]  Raman K. Mehra,et al.  Joint tracking and identification with robustness against unmodeled targets , 2003, SPIE Defense + Commercial Sensing.

[3]  Neil T. Gordon,et al.  Bayesian target tracking after group pattern distortion , 1997, Optics & Photonics.

[4]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[5]  F. D. L. Torre,et al.  Designing a Metric for the Difference between Gaussian Densities , 2010 .

[6]  Hedvig Kjellström,et al.  Multi-target particle filtering for the probability hypothesis density , 2003, ArXiv.

[7]  S. Singh,et al.  Novel data association schemes for the probability hypothesis density filter , 2007, IEEE Transactions on Aerospace and Electronic Systems.

[8]  van Marie-Colette Lieshout,et al.  Markov Point Processes and Their Applications , 2000 .

[9]  Ronald Mahler,et al.  Detecting, tracking, and classifying group targets: a unified approach , 2001, SPIE Defense + Commercial Sensing.

[10]  Lyudmila Mihaylova,et al.  A novel Sequential Monte Carlo approach for extended object tracking based on border parameterisation , 2011, 14th International Conference on Information Fusion.

[11]  Ronald P. S. Mahler,et al.  Statistical Multisource-Multitarget Information Fusion , 2007 .

[12]  Branko Ristic,et al.  Beyond the Kalman Filter: Particle Filters for Tracking Applications , 2004 .

[13]  Ba-Ngu Vo,et al.  GM-PHD filter multitarget tracking in sonar images , 2006, SPIE Defense + Commercial Sensing.

[14]  David Suter,et al.  Bayesian multi-object estimation from image observations , 2009, 2009 12th International Conference on Information Fusion.

[15]  Ba-Ngu Vo,et al.  The Cardinality Balanced Multi-Target Multi-Bernoulli Filter and Its Implementations , 2009, IEEE Transactions on Signal Processing.

[16]  D. Clark,et al.  PHD filter multi-target tracking in 3D sonar , 2005, Europe Oceans 2005.

[17]  Daniel E. Clark,et al.  Convergence results for the particle PHD filter , 2006, IEEE Transactions on Signal Processing.

[18]  K. Punithakumar,et al.  Multiple-model probability hypothesis density filter for tracking maneuvering targets , 2004, IEEE Transactions on Aerospace and Electronic Systems.

[19]  Sumeetpal S. Singh,et al.  Convergence of the SMC Implementation of the PHD Filte , 2006 .

[20]  Umut Orguner.  CPHD filter derivation for extended targets , 2010 .

[21]  Sumeetpal S. Singh,et al.  Sequential monte carlo implementation of the phd filter for multi-target tracking , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

[22]  Daniel E. Clark,et al.  First-moment filters for spatial independent cluster processes , 2010, Defense + Commercial Sensing.

[23]  Simon J. Godsill,et al.  Gaussian mixture implementations of phd filters for non-linear dynamical models , 2008 .

[24]  Yvan R. Petillot,et al.  Detection and Tracking of Multiple Metallic Objects in Millimetre-Wave Images , 2007, International Journal of Computer Vision.

[25]  Thia Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software , 2001 .

[26]  Ba-Ngu Vo,et al.  Bayesian Multi-Object Filtering With Amplitude Feature Likelihood for Unknown Object SNR , 2010, IEEE Transactions on Signal Processing.

[27]  Y. Ho,et al.  A Bayesian approach to problems in stochastic estimation and control , 1964 .

[28]  Rasmus Waagepetersen,et al.  Nearest-neighbour Markov point processes , 2003 .

[29]  B. Vo,et al.  Data Association and Track Management for the Gaussian Mixture Probability Hypothesis Density Filter , 2009, IEEE Transactions on Aerospace and Electronic Systems.

[30]  Daniel E. Clark,et al.  Fa a di Bruno's formula for variational calculus , 2013 .

[31]  J. Neyman,et al.  Statistical Approach to Problems of Cosmology , 1958 .

[32]  |Marcus Baum,et al.  Random Hypersurface Models for extended object tracking , 2009, 2009 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[33]  Branko Ristic,et al.  Calibration of Multi-Target Tracking Algorithms Using Non-Cooperative Targets , 2013, IEEE Journal of Selected Topics in Signal Processing.

[34]  Aaron D. Lanterman,et al.  Probability hypothesis density-based multitarget tracking with bistatic range and Doppler observations , 2005 .

[35]  Wolfgang Koch,et al.  On Bayesian Tracking of Extended Objects , 2006, 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems.

[36]  Christian Lundquist,et al.  Tracking rectangular and elliptical extended targets using laser measurements , 2011, 14th International Conference on Information Fusion.

[37]  Hedvig Kjellström,et al.  Tracking Random Sets of Vehicles in Terrain , 2003, 2003 Conference on Computer Vision and Pattern Recognition Workshop.

[38]  P. Mahalanobis.  On the generalized distance in statistics , 1936 .

[39]  Karl Granström,et al.  A phd Filter for Tracking Multiple Extended Targets Using Random Matrices , 2012, IEEE Transactions on Signal Processing.

[40]  Ba-Ngu Vo,et al.  A Consistent Metric for Performance Evaluation of Multi-Object Filters , 2008, IEEE Transactions on Signal Processing.

[41]  Ronald P. S. Mahler,et al.  Extended first-order Bayes filter for force aggregation , 2002, SPIE Defense + Commercial Sensing.

[42]  Kim B. Housewright,et al.  Derivation and evaluation of improved tracking filter for use in dense multitarget environments , 1974, IEEE Trans. Inf. Theory.

[43]  Petar M. Djuric,et al.  Gaussian sum particle filtering , 2003, IEEE Trans. Signal Process..

[44]  Daniel E. Clark,et al.  On the ordering of the sensors in the iterated-corrector probability hypothesis density (PHD) filter , 2011, Defense + Commercial Sensing.

[45]  Ronald P. S. Mahler,et al.  PHD filters for nonstandard targets, I: Extended targets , 2009, 2009 12th International Conference on Information Fusion.

[46]  Y. Bar-Shalom.  Tracking and data association , 1988 .

[47]  Aaron D. Lanterman,et al.  Multitarget tracking using multiple bistatic range measurements with probability hypothesis densities , 2004, SPIE Defense + Commercial Sensing.

[48]  Petar M. Djuric,et al.  Gaussian particle filtering , 2003, IEEE Trans. Signal Process..

[49]  Daryl J. Daley,et al.  An Introduction to the Theory of Point Processes , 2013 .

[50]  D.E. Clark,et al.  Data Association for the PHD Filter , 2005, 2005 International Conference on Intelligent Sensors, Sensor Networks and Information Processing.

[51]  Ba-Ngu Vo,et al.  Convergence Analysis of the Gaussian Mixture PHD Filter , 2007, IEEE Transactions on Signal Processing.

[52]  Christian Lundquist,et al.  Estimating the shape of targets with a PHD filter , 2011, 14th International Conference on Information Fusion.

[53]  John Stein,et al.  An optimal tracking filter for processing sensor data of imprecisely determined origin in surveillance systems , 1971, CDC 1971.

[54]  Uwe D. Hanebeck,et al.  Extended Object and Group Tracking: A Comparison of Random Matrices and Random Hypersurface Models , 2010, GI Jahrestagung.

[55]  Ba-Ngu Vo,et al.  Convolution Kernels based Sequential Monte Carlo Approximation of the Probability Hypothesis Density (PHD) Filter , 2007, 2007 Information, Decision and Control.

[56]  Uwe D. Hanebeck,et al.  Extended object tracking based on combined set-theoretic and stochastic fusion , 2009, 2009 12th International Conference on Information Fusion.

[57]  Fredrik Gustafsson,et al.  Estimating polynomial structures from radar data , 2010, 2010 13th International Conference on Information Fusion.

[58]  Ba-Ngu Vo,et al.  A Gaussian Mixture PHD Filter for Nonlinear Jump Markov Models , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.

[59]  A.D. Lanterman,et al.  A probability hypothesis density-based multitarget tracker using multiple bistatic range and velocity measurements , 2004, Thirty-Sixth Southeastern Symposium on System Theory, 2004. Proceedings of the.

[60]  Ba-Ngu Vo,et al.  Probability hypothesis density filter versus multiple hypothesis tracking , 2004, SPIE Defense + Commercial Sensing.

[61]  Ba-Ngu Vo,et al.  The GM-PHD Filter Multiple Target Tracker , 2006, 2006 9th International Conference on Information Fusion.

[62]  Robert Tibshirani,et al.  An Introduction to the Bootstrap , 1994 .

[63]  Samuel S. Blackman,et al.  Multiple-Target Tracking with Radar Applications , 1986 .

[64]  Branko Ristic,et al.  Particle filter for sequential detection and tracking of an extended object in clutter , 2012, 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO).

[65]  Ronald P. S. Mahler.  The multisensor PHD filter: I. General solution via multitarget calculus , 2009, Defense + Commercial Sensing.

[66]  Daniel E. Clark,et al.  Bayesian multiple target tracking in forward scan sonar images using the PHD filter , 2005 .

[67]  David Suter,et al.  Bayesian integration of audio and visual information for multi-target tracking using a CB-member filter , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[68]  B. Vo,et al.  Improved Probability Hypothesis Density (PHD) Filter for Multitarget Tracking , 2005, 2005 3rd International Conference on Intelligent Sensing and Information Processing.

[69]  Thiagalingam Kirubarajan,et al.  Data association combined with the probability hypothesis density filter for multitarget tracking , 2004, SPIE Defense + Commercial Sensing.

[70]  G. Rota.  The Number of Partitions of a Set , 1964 .

[71]  B. Anderson,et al.  Optimal Filtering , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[72]  Ba-Ngu Vo,et al.  Tracking an unknown time-varying number of speakers using TDOA measurements: a random finite set approach , 2006, IEEE Transactions on Signal Processing.

[73]  Daniel E. Clark,et al.  Extended object filtering using spatial independent cluster processes , 2010, 2010 13th International Conference on Information Fusion.

[74]  Antonio Cantoni,et al.  On multi-Bernoulli approximations to the Bayes multi-target filter , 2007 .

[75]  Ba-Ngu Vo,et al.  Gaussian Particle Implementations of Probability Hypothesis Density Filters , 2007, 2007 IEEE Aerospace Conference.

[76]  Ba-Ngu Vo,et al.  Joint detection and tracking of multiple maneuvering targets in clutter using random finite sets , 2004, ICARCV 2004 8th Control, Automation, Robotics and Vision Conference, 2004..

[77]  Ba-Ngu Vo,et al.  Improved SMC implementation of the PHD filter , 2010, 2010 13th International Conference on Information Fusion.

[78]  Ba-Ngu Vo,et al.  Multi-Bernoulli filtering with unknown clutter intensity and sensor field-of-view , 2011, 2011 45th Annual Conference on Information Sciences and Systems.

[79]  Jeremie Houssineau,et al.  PHD filter with diffuse spatial prior on the birth process with applications to GM-PHD filter , 2010, 2010 13th International Conference on Information Fusion.

[80]  Ba-Ngu Vo,et al.  The Gaussian Mixture Probability Hypothesis Density Filter , 2006, IEEE Transactions on Signal Processing.

[81]  Reiner S. Thomä,et al.  Multiple target tracking by a distributed UWB sensor network based on the PHD filter , 2012, 2012 15th International Conference on Information Fusion.

[82]  H. Sorenson,et al.  Nonlinear Bayesian estimation using Gaussian sum approximations , 1972 .

[83]  Dominique Heurguier,et al.  Multi-target PHD filtering: proposition of extensions to the multi-sensor case , 2010 .

[84]  R. Mahler.  Multitarget Bayes filtering via first-order multitarget moments , 2003 .

[85]  Kumaradevan Punithakumar,et al.  A sequential Monte Carlo probability hypothesis density algorithm for multitarget track-before-detect , 2005, SPIE Optics + Photonics.

[86]  Simon J. Godsill,et al.  Poisson models for extended target and group tracking , 2005, SPIE Optics + Photonics.

[87]  A. Doucet,et al.  Sequential Monte Carlo methods for multitarget filtering with random finite sets , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[88]  Zhongliang Jing,et al.  Bearing-only multi-target location Based on Gaussian Mixture PHD filter , 2007, 2007 10th International Conference on Information Fusion.

[89]  D.E. Clark,et al.  An Efficient Track Management Scheme for the Gaussian-Mixture Probability Hypothesis Density Tracker , 2006, 2006 Fourth International Conference on Intelligent Sensing and Information Processing.

[90]  Christian Lundquist,et al.  Extended target tracking with a cardinalized probability hypothesis density filter , 2011, 14th International Conference on Information Fusion.

[91]  D. Stoyan,et al.  Stochastic Geometry and Its Applications , 1989 .

[92]  D.J. Salmond,et al.  Mixture Reduction Algorithms for Point and Extended Object Tracking in Clutter , 2009, IEEE Transactions on Aerospace and Electronic Systems.

[93]  T. E. Harris,et al.  The Theory of Branching Processes. , 1963 .

[94]  Christian Lundquist,et al.  A Gaussian mixture PHD filter for extended target tracking , 2010, 2010 13th International Conference on Information Fusion.

[95]  Syed Ahmed Pasha,et al.  A Gaussian Mixture PHD Filter for Jump Markov System Models , 2009, IEEE Transactions on Aerospace and Electronic Systems.

[96]  Dietrich Fränken,et al.  Tracking of Extended Objects and Group Targets Using Random Matrices , 2008, IEEE Transactions on Signal Processing.

[97]  Branko Ristic,et al.  Bernoulli filter for joint detection and tracking of an extended object in clutter , 2013 .

[98]  M. Vihola.  Rao-blackwellised particle filtering in random set multitarget tracking , 2007, IEEE Transactions on Aerospace and Electronic Systems.

[99]  Hugh F. Durrant-Whyte,et al.  A new method for the nonlinear transformation of means and covariances in filters and estimators , 2000, IEEE Trans. Autom. Control..

[100]  David Suter,et al.  Joint Detection and Estimation of Multiple Objects From Image Observations , 2010, IEEE Transactions on Signal Processing.

[101]  Ronald P. S. Mahler,et al.  Particle-systems implementation of the PHD multitarget-tracking filter , 2003, SPIE Defense + Commercial Sensing.

[102]  Dietrich Fränken,et al.  Advances on tracking of extended objects and group targets using random matrices , 2009, 2009 12th International Conference on Information Fusion.

[103]  B. Vo,et al.  A closed-form solution for the probability hypothesis density filter , 2005, 2005 7th International Conference on Information Fusion.

[104]  J. E. Moyal.  The general theory of stochastic population processes , 1962 .

[105]  S. Godsill,et al.  Multi-Object Tracking of Sinusoidal Components in Audio with the Gaussian Mixture Probability Hypothesis Density Filter , 2007, 2007 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics.

[106]  D. Salmond,et al.  Spatial distribution model for tracking extended objects , 2005 .

[107]  Ba-Ngu Vo,et al.  Multi-Bernoulli based track-before-detect with road constraints , 2012, 2012 15th International Conference on Information Fusion.

[108]  G. Matheron.  Random Sets and Integral Geometry , 1976 .

[109]  Y. Bar-Shalom,et al.  Tracking in a cluttered environment with probabilistic data association , 1975, Autom..

[110]  Gregory D. Hager,et al.  Probabilistic data association methods in visual tracking of groups , 2004, CVPR 2004.

[111]  R. Mahler,et al.  PHD filters of higher order in target number , 2006, IEEE Transactions on Aerospace and Electronic Systems.

[112]  Matti Vihola,et al.  Random set particle filter for bearings-only multitarget tracking , 2005, SPIE Defense + Commercial Sensing.

[113]  Ba-Ngu Vo,et al.  Adaptive Target Birth Intensity for PHD and CPHD Filters , 2012, IEEE Transactions on Aerospace and Electronic Systems.

[114]  Branko Ristic,et al.  Particle filter for joint estimation of multi-object dynamic state and multi-sensor bias , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[115]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[116]  Fuzhen Zhang.  The Schur complement and its applications , 2005 .

[117]  Ba-Ngu Vo,et al.  CPHD Filtering With Unknown Clutter Rate and Detection Profile , 2011, IEEE Transactions on Signal Processing.

[118]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[119]  A. Baddeley,et al.  Nearest-Neighbour Markov Point Processes and Random Sets , 1989 .

[120]  N. Ikoma,et al.  Tracking of feature points in image sequence by SMC implementation of PHD filter , 2004, SICE 2004 Annual Conference.

[121]  Neil J. Gordon,et al.  Group tracking with limited sensor resolution and finite field of view , 2000, SPIE Defense + Commercial Sensing.

[122]  Ba-Ngu Vo,et al.  Closed Form PHD Filtering for Linear Jump Markov Models , 2006, 2006 9th International Conference on Information Fusion.

[123]  Daniel E. Clark,et al.  Generalized PHD filters via a general chain rule , 2012, 2012 15th International Conference on Information Fusion.

[124]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[125]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[126]  Uwe D. Hanebeck,et al.  Extended object and group tracking with Elliptic Random Hypersurface Models , 2010, 2010 13th International Conference on Information Fusion.

[127]  Branko Ristic,et al.  Calibration of tracking systems using detections from non-cooperative targets , 2012, 2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF).

[128]  Joaquim Salvi,et al.  SLAM With Dynamic Targets via Single-Cluster PHD Filtering , 2013, IEEE Journal of Selected Topics in Signal Processing.

[129]  H. Sorenson,et al.  Recursive bayesian estimation using gaussian sums , 1971 .

[130]  Christian Lundquist,et al.  An Extended Target CPHD Filter and a Gamma Gaussian Inverse Wishart Implementation , 2013, IEEE Journal of Selected Topics in Signal Processing.

[131]  Brian D. Ripley,et al.  Locally Finite Random Sets: Foundations for Point Process Theory , 1976 .

[132]  Ba-Ngu Vo,et al.  Analytic Implementations of the Cardinalized Probability Hypothesis Density Filter , 2007, IEEE Transactions on Signal Processing.

[133]  Joaquim Salvi,et al.  SLAM with single cluster PHD filters , 2012, 2012 IEEE International Conference on Robotics and Automation.