Finite resolution multitarget tracking

Target tracking algorithms have to operate in an environment of uncertain measurement origin, due to the presence of randomly detected target measurements as well as clutter measurements from unwanted random scatterers. A majority of Bayesian multi-target tracking algorithms suffer from computational complexity which is exponential in the number of tracks and the number of shared measurements. The Linear Multi-target (LM) tracking procedure is a Bayesian multi-target tracking approximation with complexity which is linear in the number of tracks and the number of shared measurements. It also has a much simpler structure than the "optimal" Bayesian multi-target tracking, with apparently negligible decrease in performance. A vast majority of target tracking algorithms have been developed with the assumption of infinite sensor resolution, where a measurement can have only one source. This assumption is not valid for real sensors, such as radars. This paper presents a multi-target tracking algorithm which removes this restriction. The procedure utilizes a simple structure of LM tracking procedure to obtain a LM Finite Resolution (LMfr) tracking procedure which is much simpler than the previously published efforts. Instead of calculating the probability of measurement merging for each combination of potentially merging targets, we evaluate only one merging hypotheses for each measurement and each track. A simulation study is presented which compares LMfr-IPDA with LM-IPDA and IPDA target tracking in a cluttered environment utilizing a finite resolution sensor with five crossing targets. The study concentrates on the false track discrimination performance and the track retention capabilities.

[1]  Yaakov Bar-Shalom,et al.  Estimation and Tracking: Principles, Techniques, and Software , 1993 .

[2]  S. Stankovic,et al.  Integrated probabilistic data association (IPDA) , 1992, [1992] Proceedings of the 31st IEEE Conference on Decision and Control.

[3]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[4]  Yaakov Bar-Shalom,et al.  Multitarget-Multisensor Tracking , 1995 .

[5]  D. Muslicki,et al.  Integrated track splitting suite of target tracking filters , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

[6]  Donald Reid.  An algorithm for tracking multiple targets , 1978 .

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

[8]  Darko Musicki,et al.  Joint Integrated Probabilistic Data Association: JIPDA, IEEE Transactions Aerospace & Electronic Systems. , 2004 .

[9]  Yaakov Bar-Shalom,et al.  Multitarget/Multisensor Tracking: Applications and Advances -- Volume III , 2000 .

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

[11]  Yaakov Bar-Shalom,et al.  Sonar tracking of multiple targets using joint probabilistic data association , 1983 .

[12]  N. K. Bose,et al.  Multitarget tracking in clutter: fast algorithms for data association , 1993 .

[13]  Y. Bar-Shalom,et al.  The interacting multiple model algorithm for systems with Markovian switching coefficients , 1988 .

[14]  Mark R. Morelande,et al.  An integrated particle filter for a finite resolution sensor , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[15]  Robin J. Evans,et al.  Linear joint integrated probabilistic data association - LJIPDA , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[16]  Yaakov Bar-Shalom,et al.  Interacting multiple model tracking with target amplitude feature , 1993 .

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

[18]  R. Evans,et al.  Clutter map information for data association and track initialization , 2004, IEEE Transactions on Aerospace and Electronic Systems.

[19]  Robin J. Evans,et al.  Integrated probabilistic data association-finite resolution , 1995, Autom..