An integrated particle filter for a finite resolution sensor

A particle filtering procedure for automatic track formation using measurements from a finite resolution sensor is proposed. Automatic track formation is performed by recursively computing the posterior probability of target existence, i.e., a measure of our belief that the track is following a target, and the posterior distribution of the target state conditional on existence. An auxiliary particle filter is developed for this purpose under quite general conditions. This algorithm requires computation of the measurement likelihood, closed-form expressions for which are generally unavailable. A procedure for computing the likelihood for the case where the target dynamic and measurement equations are linear/normal is given. Simulations are used to demonstrate the superiority of the proposed method over an existing method based on normal approximations.

[1]  R. Bucy,et al.  Realization of Optimum Discrete-Time Nonlinear Estimators, , 1971 .

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

[3]  M. Kendall,et al.  The Advanced Theory of Statistics: Volume 1, Distribution Theory , 1978 .

[4]  Robin J. Evans,et al.  Integrated probabilistic data association , 1994, IEEE Trans. Autom. Control..

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

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

[7]  M. Kendall,et al.  The Advanced Theory of Statistics, Vol. 1: Distribution Theory , 1959 .

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

[9]  M. Pitt,et al.  Filtering via Simulation: Auxiliary Particle Filters , 1999 .

[10]  Christian P. Robert,et al.  Monte Carlo Statistical Methods , 2005, Springer Texts in Statistics.

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

[12]  Darko Musicki,et al.  Joint Integrated Probabilistic Data Association - JIPDA , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).

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

[14]  G.V. Trunk,et al.  Track Initiation of Occasionally Unresolved Radar Targets , 1981, IEEE Transactions on Aerospace and Electronic Systems.

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

[16]  Neil J. Gordon,et al.  Editors: Sequential Monte Carlo Methods in Practice , 2001 .

[17]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.

[18]  A. Farina,et al.  Effects of cross-covariance and resolution on track association , 2000, Proceedings of the Third International Conference on Information Fusion.