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If a tree falls in the woods, it does make a sound: multiple-hypothesis tracking with undetected target births

机译:如果一棵树落在树林中,它会发出声音:多重假设跟踪,未检测到目标出生

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This paper introduces a generalization of the multiple-hypothesis tracking (MHT) formalism for multitarget tracking. To our knowledge, MHT treatments in the literature do not consider undetected target birth events. Their inclusion leads to an interesting extension to the MHT recursion and necessitates aggregation over indistinguishable global hypotheses. We show that the MHT recursion factors, enabling track-oriented MHT (TO-MHT), albeit with clusters of indistinguishable undetected births. The treatment requires a distinction between those targets that are eventually detected (we call these unnoticed targets) and those that are never detected (we call these ghost targets). While the formulation appears more complex, there is structure to the solution that can be exploited, resulting in the same number of relevant track hypotheses for detected targets as in the classical TO-MHT solution. In the time-invariant case, the solution simplifies further because we need not consider unnoticed targets and there is a fixed structure to the ghost target solution.
机译:本文介绍了用于多目标跟踪的多假设跟踪(MHT)形式主义的概括。据我们所知,文献中的MHT治疗并未考虑未发现的目标出生事件。它们的包含导致对MHT递归的有趣扩展,并有必要在无法区分的全局假设上进行汇总。我们显示,MHT递归因子可以实现面向轨道的MHT(TO-MHT),尽管具有无法区分的未检测到的出生簇。这种处理方法需要区分最终被检测到的那些目标(我们称为这些未被注意的目标)和从未被检测到的那些目标(我们称为这些幻影目标)。尽管公式看起来更复杂,但可以利用该解决方案的结构,从而导致与经典TO-MHT解决方案中检测到的目标相同数量的相关跟踪假设。在时不变的情况下,该解决方案进一步简化了,因为我们不需要考虑未被注意的目标,并且幻影目标解决方案具有固定的结构。

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