Unified cardinalized probability hypothesis density filters for extended targets and unresolved targets

The unified cardinalized probability hypothesis density (CPHD) filters for extended targets and unresolved targets are proposed. The theoretically rigorous measurement-update equations for the proposed filters are derived according to the theory of random finite set (RFS) and finite-set statistics (FISST). By assuming that the predicted distributions of the extended targets and unresolved targets and the distribution of the clutter are Poisson, the exact extended-target and unresolved-target CPHD correctors reduce to the exact extended-target and unresolved-target PHD correctors, respectively. Since the exact CPHD and PHD corrector equations involve with a number of operations that grow exponentially with the number of measurements, the computationally tractable approximations for them are presented, which can be used when the extended targets and the unresolved targets are not too close together and the clutter density is not too large. Monte Carlo simulation results show that the approximate extended-target and unresolved-target CPHD filters, respectively, outperform the approximate extended-target and unresolved-target PHD filters a lot in estimating the target number and states, although the computational requirement of the CPHD filters is more expensive than that of the PHD filters.

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

[2]  Kuo-Chu Chang,et al.  Joint probabilistic data association for multitarget tracking with possibly unresolved measurements and maneuvers , 1984 .

[3]  Fred Daum,et al.  Importance of resolution in multiple-target tracking , 1994, Defense, Security, and Sensing.

[4]  Taek Lyul Song,et al.  A probabilistic nearest neighbor filter algorithm for tracking in a clutter environment , 2005, Signal Process..

[5]  P. Laguna,et al.  Signal Processing , 2002, Yearbook of Medical Informatics.

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

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

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

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

[10]  Zhongliang Jing,et al.  Gaussian mixture CPHD filter with gating technique , 2009, Signal Process..

[11]  Neil J. Gordon,et al.  Group and extended object tracking , 1999 .

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

[13]  E. A. Bloem,et al.  Bayesian tracking of two possibly unresolved maneuvering targets , 2007, IEEE Transactions on Aerospace and Electronic Systems.

[14]  H. B. Mitchell,et al.  A "soft" assignment algorithm for multiple target tracking , 2001, Signal Process..

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

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

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

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

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

[20]  Yimin Liu,et al.  Deinterleaving pulse trains in unconventional circumstances using multiple hypothesis tracking algorithm , 2010, Signal Process..

[21]  Martin Ulmke,et al.  Joint probabilistic data association filter for partially unresolved target groups , 2010, 2010 13th International Conference on Information Fusion.

[22]  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.

[23]  Lang Hong,et al.  An interacting multipattern joint probabilistic data association (IMP-JPDA) algorithm for multitarget tracking , 2000, Signal Process..

[24]  W. Koch,et al.  Multiple hypothesis track maintenance with possibly unresolved measurements , 1997, IEEE Transactions on Aerospace and Electronic Systems.

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

[26]  Oliver E. Drummond,et al.  A bibliography of cluster (group) tracking , 2004, SPIE Defense + Commercial Sensing.

[27]  Yakov Bar-Shalom,et al.  Multitarget-Multisensor Tracking: Principles and Techniques , 1995 .

[28]  Ronald P. S. Mahler,et al.  CPHD filters for superpositional sensors , 2009, Optical Engineering + Applications.

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

[30]  Chongzhao Han,et al.  Sequential Monte Carlo implementation and state extraction of the group probability hypothesis density filter for partly unresolvable group targets-tracking problem , 2010 .

[31]  I. R. Goodman,et al.  Mathematics of Data Fusion , 1997 .

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

[33]  X. Rong Li,et al.  Survey of maneuvering target tracking: dynamic models , 2000, SPIE Defense + Commercial Sensing.

[34]  Daniel Hauschildt.  Gaussian mixture implementation of the cardinalized probability hypothesis density filter for superpositional sensors , 2011, 2011 International Conference on Indoor Positioning and Indoor Navigation.

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

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

[37]  Felipe Cucker,et al.  Emergent Behavior in Flocks , 2007, IEEE Transactions on Automatic Control.