首页> 外文会议>Conference on Signal and Data Processing of Small Targets 2004; 20040413-20040415; Orlando,FL; US >Data association hypothesis evaluation for i.i.d. but non-Poisson multiple target tracking
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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:本文讨论了针对多目标跟踪问题的一般类别的数据关联假设的评估。我们假设目标数量是未知的,并且假设目标数量是多少,则联合目标状态分布会形成一个独立的,均布的(i.i.d.)概率分布系统。我们对目标数量的先验概率分布不一定是泊松的情况特别感兴趣。我们将证明,泊松假设不仅对于常用的标准乘法假设评估公式而言是足够的,而且也是必要的。因此,我们声称标准乘法假设评估公式的使用暗示或隐含了泊松假设。我们还将研究每个测量集中错误警报数量的泊松假设。

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