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Data association hypothesis evaluation for i.i.d. but non-Poisson multiple target tracking

机译:数据关联假设评估。但非泊松多目标跟踪

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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.
机译:本文讨论了对多个目标跟踪问题一般类别的数据关联假设的评估。我们假设目标的数量未知,并且给定目标的数量,联合目标状态分布形成一个独立的,相同分布(i.i.d.)概率分布的系统。我们对目标数量不一定是泊松的情况特别感兴趣。我们将表明,泊松假设不仅足够,而且还需要常用的标准乘法假设评估公式。因此,我们声称使用标准乘法假设评估公式意味着明确或隐含地,泊松假设。我们还将在每个测量集中的误报的数量上检查泊松假设。

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