Permutation invariance in Bayesian estimation of two targets that maneuver in and out formation flight

In theory, a good particle filter allows to approximate the exact Bayesian filter solution arbitrarily well. This has motivated a strong and successful development of particle filtering approaches towards target tracking. Literature also shows that successful multiple maneuvering target tracking seems to depend on the adoption of some suitable heuristics in handling permutation invariance. According to the exact Bayesian joint conditional density however, there is no requirement to introduce any of these heuristics. In order to improve the insight for this kind of problem, this paper studies which role permutation invariance plays in the exact joint conditional density of two targets that may maneuver in and out formation flight given potentially noisy, missing and false measurements.

[1]  S. Godsill,et al.  Monte Carlo filtering for multi target tracking and data association , 2005, IEEE Transactions on Aerospace and Electronic Systems.

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

[3]  Simon J. Godsill,et al.  On sequential simulation-based methods for Bayesian filtering , 1998 .

[4]  A. Hero,et al.  Multitarget tracking using the joint multitarget probability density , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[5]  Fredrik Gustafsson,et al.  Monte Carlo data association for multiple target tracking , 2001 .

[6]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[7]  P. Pérez,et al.  Tracking multiple objects with particle filtering , 2002 .

[8]  N. Gordon A hybrid bootstrap filter for target tracking in clutter , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[9]  Henk A. P. Blom,et al.  Joint Particle Filtering of Multiple Maneuvering Targets From Unassociated Measurements , 2006, J. Adv. Inf. Fusion.

[10]  Hans Driessen,et al.  Tracking closely spaced targets: Bayes outperformed by an approximation? , 2008, 2008 11th International Conference on Information Fusion.

[11]  Hans Driessen,et al.  The mixed labeling problem in multi target particle filtering , 2007, 2007 10th International Conference on Information Fusion.

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

[13]  A. Marrs,et al.  A Bayesian approach to multi-target tracking and data fusion with out-of-sequence measurements , 2001 .

[14]  Henk A. P. Blom,et al.  Hybrid SIR joint particle filtering under limited sensor resolution , 2007, 2007 10th International Conference on Information Fusion.

[15]  N. Gordon A hybrid bootstrap filter for target tracking in clutter , 1995, IEEE Transactions on Aerospace and Electronic Systems.

[16]  D. Rubin Using the SIR algorithm to simulate posterior distributions , 1988 .

[17]  Bing Chen,et al.  Tracking of multiple maneuvering targets in clutter using IMM/JPDA filtering and fixed-lag smoothing , 2001, Autom..

[18]  Henk A. P. Blom,et al.  Interacting multiple model joint probabilistic data association avoiding track coalescence , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[19]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[20]  Hans Driessen,et al.  MAP estimation in particle filter tracking , 2008 .

[21]  D. J. Salmond,et al.  Tracking and identification for closely spaced objects in clutter , 1997, 1997 European Control Conference (ECC).

[22]  D. Avitzour Stochastic simulation Bayesian approach to multitarget tracking , 1995 .