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Proceedings Paper

Efficient multitarget particle filters for ground target tracking
Author(s): Shawn M. Herman; Sean E. Roberts
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Paper Abstract

Many factors make the ground target tracking problem decidedly nonlinear and non-Gaussian. Because these factors can lead to a multimodal posterior density, a Bayesian filtering solution is appropriate. In the last decade, the particle filter has emerged as a Bayesian inference technique that is both powerful and simple to implement. In this work, we demonstrate the necessity of using multiple-target particle filters when two or more tracks are linked through measurement contention. We also develop an efficient way to implement these filters by adaptively managing the type of particle filters, the number of particles, and the enumeration of hypotheses during data association. Using simulated data, we compare the run-time of our adaptive particle filter algorithm to the run-times of two baseline particle filters, to demonstrate that our design mitigates the increase in computation required when performing joint multitarget tracking.

Paper Details

Date Published: 15 September 2005
PDF: 15 pages
Proc. SPIE 5913, Signal and Data Processing of Small Targets 2005, 59130T (15 September 2005); doi: 10.1117/12.617310
Show Author Affiliations
Shawn M. Herman, Numerica Corp. (United States)
Sean E. Roberts, Numerica Corp. (United States)

Published in SPIE Proceedings Vol. 5913:
Signal and Data Processing of Small Targets 2005
Oliver E. Drummond, Editor(s)

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