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Data Association in Target Tracking Using Priority Queues

机译:使用优先级队列的目标跟踪中的数据关联

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Data association is one of the main components of target tracking. While, in its simplest form, data association links a list of tracks to a list of measurements or links two lists of measurements (2-D association), the more complex problem involves assignment of multiple number of such lists (S-D association where S ≥ 3). In target tracking, the presence of false detections (false alarms) and the absence of detections from some targets (missed detections) complicate the problem of data association further. In this work, we explore the possibility of applying track ordering in priority queues to solve the association problem more efficiently. The basic component of our algorithm is to form priority queues by permutations of the tracks. Each queue is served on a first-come-first-served basis, i.e., each track is assigned to the best measurement available based on its turn in the queue. It can be shown that the best solution to the 2-D problem can be obtained from one of these queues. However, the solution is computationally expensive even for a moderate number of targets. In this paper we show that due to redundancy only a small fraction of the total number of permutations is required to be evaluated to obtain the best solution.
机译:数据关联是目标跟踪的主要组成部分之一。虽然以最简单的形式,数据关联将轨迹列表链接到测量列表,或者链接两个测量列表(2-D关联),但更复杂的问题涉及分配多个这样的列表(SD关联,其中S≥ 3)。在目标跟踪中,错误检测(错误警报)的存在和某些目标的检测缺失(丢失的检测)使数据关联的问题进一步复杂化。在这项工作中,我们探索了在优先级队列中应用轨道排序以更有效地解决关联问题的可能性。我们算法的基本组成部分是通过轨道的排列形成优先级队列。每个队列均以先到先得的方式服务,即,根据轨道中的轮流,将每个轨道分配给可用的最佳测量。可以证明,可以从这些队列之一中获得对二维问题的最佳解决方案。但是,即使对于中等数量的目标,该解决方案在计算上也很昂贵。在本文中,我们表明由于冗余,只需要评估排列总数的一小部分即可获得最佳解决方案。

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