Artificial learning approaches for multitarget tracking

Douglas Blount; Michael A. Kouritzin; Jesse D. McCrosky

doi:10.1117/12.542674

21 September 2004 Artificial learning approaches for multitarget tracking

Douglas Blount, Michael A. Kouritzin, Jesse D. McCrosky

Proceedings Volume 5426, Automatic Target Recognition XIV; (2004) https://doi.org/10.1117/12.542674
Event: Defense and Security, 2004, Orlando, Florida, United States

Abstract

A hybrid weighted/interacting particle filter, the selectively resampling particle (SERP) filter, is used to detect and track an unknown number of independent targets on a one-dimensional "racetrack" domain. The targets evolve in a nonlinear manner. The observations model a sensor positioned above the racetrack. The observation data takes the form of a discretized image of the racetrack, in which each discrete segment has a value depending both upon the presence or absence of targets in the corresponding portion of the domain, and upon lognormal noise. The SERP filter provides a conditional distribution approximated by particle simulations. After each observation is processed, the SERP filter selectively resamples its particles in a pairwise fashion, based on their relative likelihood. We consider a reinforcement learning approach to control this resampling. We compare two different ways of applying the filter to the problem: the signal measure approach and the model selection approach. We present quantitative results of the ability of the filter to detect and track the targets, for each of the techniques. Comparisons are made between the signal measure and model selection approaches, and between the dynamic and static resampling control techniques.

Citation Download Citation

Douglas Blount, Michael A. Kouritzin, and Jesse D. McCrosky "Artificial learning approaches for multitarget tracking", Proc. SPIE 5426, Automatic Target Recognition XIV, (21 September 2004); https://doi.org/10.1117/12.542674

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