Feedback particle filter-based multiple target tracking using bearing-only measurements

This paper describes the joint probabilistic data association-feedback particle filter (JPDA-FPF) introduced in our earlier paper [1]. The JPDA-FPF is based on the feedback particle filter concept (see [2],[3]). A remarkable feature of the JPDA-FPF algorithm is its innovation error-based feedback structure, even with data association uncertainty in the general nonlinear case. The classical Kalman filter-based joint probabilistic data association filter (JPDAF) is shown to be a special case of the JPDA-FPF. A multiple target tracking application is presented: In the application, bearing only measurements with multiple sensors are used to track targets in the presence of data association uncertainty. It is shown that the algorithm is successfully able to track targets with significant uncertainty in initial estimate, and even in the presence of certain “track coalescence” scenarios.

[1]  Thiagalingam Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation , 2001 .

[2]  Fred Daum,et al.  Generalized particle flow for nonlinear filters , 2010, Defense + Commercial Sensing.

[3]  Sean P. Meyn,et al.  Multivariable feedback particle filter , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[4]  Y. Bar-Shalom,et al.  The probabilistic data association filter , 2009, IEEE Control Systems.

[5]  Tao Yang,et al.  Joint probabilistic data association-feedback particle filter for multiple target tracking applications , 2012, 2012 American Control Conference (ACC).

[6]  Henk A. P. Blom,et al.  Probabilistic data association avoiding track coalescence , 2000, IEEE Trans. Autom. Control..

[7]  A. Budhiraja,et al.  A survey of numerical methods for nonlinear filtering problems , 2007 .

[8]  Darryl Morrell,et al.  Sequential Monte Carlo Methods for Tracking Multiple Targets With Deterministic and Stochastic Constraints , 2008, IEEE Transactions on Signal Processing.

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

[10]  S. Shankar Sastry,et al.  Markov Chain Monte Carlo Data Association for Multi-Target Tracking , 2009, IEEE Transactions on Automatic Control.

[11]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

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

[13]  T. Singh,et al.  Uncertainty Propagation for Nonlinear Dynamic Systems Using Gaussian Mixture Models , 2008 .

[14]  Sean P. Meyn,et al.  A mean-field control-oriented approach to particle filtering , 2011, Proceedings of the 2011 American Control Conference.

[15]  Thia Kirubarajan,et al.  Probabilistic data association techniques for target tracking in clutter , 2004, Proceedings of the IEEE.

[16]  S. Herman,et al.  A Comparison of Methods for Estimating Track-to-Track Assignment Probabilities , 2012, IEEE Transactions on Aerospace and Electronic Systems.

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

[18]  Sean P. Meyn,et al.  Feedback particle filter with mean-field coupling , 2011, IEEE Conference on Decision and Control and European Control Conference.

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