An Extended Target CPHD Filter and a Gamma Gaussian Inverse Wishart Implementation

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 been derived by Mahler, and different implementations have been proposed recently. To achieve better estimation performance this work relaxes the Poisson assumptions of the extended target PHD filter in target and measurement numbers. A gamma Gaussian inverse Wishart mixture implementation, which is capable of estimating the target extents and measurement rates as well as the kinematic state of the target, is proposed, and it is compared to its PHD counterpart in a simulation study. The results clearly show that the CPHD filter has a more robust cardinality estimate leading to smaller OSPA errors, which confirms that the extended target CPHD filter inherits the properties of its point target counterpart.

[1]  D. Clark,et al.  Group Target Tracking with the Gaussian Mixture Probability Hypothesis Density Filter , 2007, 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information.

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

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

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

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

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

[7]  Karl Granström,et al.  Estimation and maintenance of measurement rates for multiple extended target tracking , 2012, 2012 15th International Conference on Information Fusion.

[8]  Ba-Ngu Vo,et al.  Bayesian Filtering With Random Finite Set Observations , 2008, IEEE Transactions on Signal Processing.

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

[10]  Chongzhao Han,et al.  An extended target tracking method with random finite set observations , 2011, 14th International Conference on Information Fusion.

[11]  Wolfgang Koch,et al.  Cluster tracking under kinematical constraints using random matrices , 2008, Robotics Auton. Syst..

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

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

[14]  Simon J. Godsill,et al.  The Gaussian mixture MCMC particle algorithm for dynamic cluster tracking , 2009, 2009 12th International Conference on Information Fusion.

[15]  Uwe D. Hanebeck,et al.  Shape tracking of extended objects and group targets with star-convex RHMs , 2011, 14th International Conference on Information Fusion.

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

[17]  Wolfgang Koch,et al.  Probabilistic tracking of multiple extended targets using random matrices , 2010, Defense + Commercial Sensing.

[18]  Daniel E. Clark,et al.  The PHD filter for extended target tracking with estimable extent shape parameters of varying size , 2012, 2012 15th International Conference on Information Fusion.

[19]  J.W. Koch,et al.  Bayesian approach to extended object and cluster tracking using random matrices , 2008, IEEE Transactions on Aerospace and Electronic Systems.

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

[21]  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.

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

[23]  Ba-Ngu Vo,et al.  On performance evaluation of multi-object filters , 2008, 2008 11th International Conference on Information Fusion.

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

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

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

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

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

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

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

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

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

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

[34]  Christian Lundquist,et al.  Extended Target Tracking using a Gaussian-Mixture PHD Filter , 2017, IEEE Transactions on Aerospace and Electronic Systems.

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

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

[37]  Y. Bar-Shalom,et al.  Probability hypothesis density filter for multitarget multisensor tracking , 2005, 2005 7th International Conference on Information Fusion.

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

[39]  Karl Granström,et al.  On the reduction of Gaussian inverse Wishart mixtures , 2012, 2012 15th International Conference on Information Fusion.

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

[41]  Chongzhao Han,et al.  Unified cardinalized probability hypothesis density filters for extended targets and unresolved targets , 2012, Signal Process..