Implementation of the homotopy particle filter in the JPDA and MAP-PF multi-target tracking algorithms

In a conventional particle filter, the information update step can suffer from particle degeneracy if the likelihood function is concentrated on only a few particles. The homotopy particle flow method has been developed to implement the information update in an entirely different manner by using a particle flow function to migrate the particles to regions of the target state space that provide a good representation of the posterior distribution. In this paper we demonstrate how the homotopy particle filter can be implemented with the JPDA and MAP-PF multi-target tracking algorithms, and compare performance to the conventional resampling method.

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

[2]  Peter Willett,et al.  Discussion and application of the homotopy filter , 2011, Defense + Commercial Sensing.

[3]  Fred Daum,et al.  Particle degeneracy: root cause and solution , 2011, Defense + Commercial Sensing.

[4]  Jiande Wu,et al.  Performance comparison of GPU-accelerated particle flow and particle filters , 2013, Proceedings of the 16th International Conference on Information Fusion.

[5]  Fred Daum,et al.  Nonlinear filters with log-homotopy , 2007, SPIE Optical Engineering + Applications.

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

[7]  Tao Ding,et al.  Implementation of the Daum-Huang exact-flow particle filter , 2012, 2012 IEEE Statistical Signal Processing Workshop (SSP).