Data association hypothesis evaluation for i.i.d. but non-Poisson multiple target tracking
Shozo Mori, Chee-Yee Chong · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
This paper discusses the evaluation of data association hypotheses for a general class of multiple target tracking problems. We assume that the number of targets is unknown, and that given the number of targets, the joint target state distributions form a system of independent, identically distributed (i.i.d.) probability distributions. We are particularly interested in the case where the prior probability distribution of the number of targets is not necessarily Poisson. We will show that the Poisson assumption is not only sufficient but also necessary for the commonly used standard multiplicative hypothesis evaluation formula. Consequently, we claim that the use of the standard multiplicative hypothesis evaluation formula implies, either explicitly or implicitly, the Poisson assumption. We will also examine the Poisson assumption on the number of false alarms in each measurement set.© (2004) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.