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Efficient multitarget particle filters for ground target tracking

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.