25 August 2004 Data association hypothesis evaluation for i.i.d. but non-Poisson multiple target tracking
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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.
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Shozo Mori and Chee-Yee Chong "Data association hypothesis evaluation for i.i.d. but non-Poisson multiple target tracking", Proc. SPIE 5428, Signal and Data Processing of Small Targets 2004, (25 August 2004); doi: 10.1117/12.543320; https://doi.org/10.1117/12.543320

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