Particle filtering for multitarget detection and tracking

This paper presents a particle filter approach to recursively estimating the joint multitarget probability density (JMPD) for the purposes of simultaneous multitarget detection and tracking. The JMPD is a conditional probability density that characterizes uncertainty in both target state and target number given the measurements. Estimation of the JMPD presents a formidable computational challenge due to the high dimensionality of the state space needed to explicitly model the correlations between target states and between target states and target number. We address this challenge with an importance density that is measurement directed and which adaptively factorizes the problem into a set of smaller sub-problems when possible. We demonstrate the algorithm on a set of real targets whose motion is taken from a set of military battle exercises

[1]  Jun S. Liu,et al.  Sequential Monte Carlo methods for dynamic systems , 1997 .

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

[3]  Roy L. Streit,et al.  Maximum likelihood method for probabilistic multihypothesis tracking , 1994, Defense, Security, and Sensing.

[4]  Neil J. Gordon,et al.  Efficient particle filtering for multiple target tracking with application to tracking in structured images , 2002, SPIE Defense + Commercial Sensing.

[5]  William Fitzgerald,et al.  A Bayesian approach to tracking multiple targets using sensor arrays and particle filters , 2002, IEEE Trans. Signal Process..

[6]  Henk A. P. Blom,et al.  Joint IMMPDA particle filter , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

[7]  Yaakov Bar-Shalom,et al.  Multitarget-Multisensor Tracking: Applications and Advances , 1992 .

[8]  Sumeetpal S. Singh,et al.  Sequential monte carlo implementation of the phd filter for multi-target tracking , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

[9]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[10]  Keith D. Kastella.  Maximum likelihood estimator for report-to-track association , 1993, Defense, Security, and Sensing.

[11]  Alfred O. Hero,et al.  Tracking multiple targets using a particle filter representation of the joint multitarget probability density , 2004, SPIE Optics + Photonics.

[12]  R. E. Bethel,et al.  A PDF multisensor multitarget tracker , 1998 .

[13]  H. M. Shertukde,et al.  Tracking of crossing targets with imaging sensors , 1991 .

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

[15]  Samuel S. Blackman,et al.  Multiple-Target Tracking with Radar Applications , 1986 .

[16]  K. Kastella,et al.  Event-averaged maximum likelihood estimation and mean-field theory in multitarget tracking , 1995, IEEE Trans. Autom. Control..

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

[18]  Michael Isard,et al.  BraMBLe: a Bayesian multiple-blob tracker , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[19]  Y. Bar-Shalom,et al.  Topography-based VS-IMM estimator for large-scale ground target tracking , 1999 .

[20]  Lawrence D. Stone,et al.  Bayesian Multiple Target Tracking , 1999 .

[21]  Keith D. Kastella,et al.  Practical implementation of joint multitarget probabilities , 1998, Defense, Security, and Sensing.

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

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

[24]  E. Kamen,et al.  Multiple Target Tracking based on Symmetric Measurement Equations , 1989, 1989 American Control Conference.

[25]  R. Streit,et al.  Probabilistic Multi-Hypothesis Tracking , 1995 .

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

[27]  H. Sidenbladh,et al.  Particle Filtering for Random Sets , 2003 .

[28]  S. Mori,et al.  Tracking and classifying multiple targets without a priori identification , 1986 .

[29]  Keith Kastella.  Discrimination gain to optimize detection and classification , 1997, IEEE Trans. Syst. Man Cybern. Part A.

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

[31]  George W. Stimson,et al.  Introduction to Airborne Radar , 1983 .

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

[33]  A. Doucet.  On sequential Monte Carlo methods for Bayesian filtering , 1998 .

[34]  Michael I. Miller,et al.  Conditional-mean estimation via jump-diffusion processes in multiple target tracking/recognition , 1995, IEEE Trans. Signal Process..

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