A filter for distinguishable and independent populations
A filter for distinguishable and independent populations
复制标题
用于区分可区分和独立群体的过滤器
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Daniel E. Clark
中科院分区：
文献类型：
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作者：
E. Delande;J. Houssineau;Daniel E. Clark
本文介绍了一种多目标滤波器，用于解决涉及多目标的联合检测/跟踪问题，该滤波器源自随机总体的新型贝叶斯估计框架。完全概率的性质，可区分和独立的随机种群（DISP）的过滤器利用两个排他性的概率表示的潜在目标。可区分的目标是那些通过过去的探测获得个人信息的目标;它们由个人轨迹表示。不可区分的目标是那些没有个人信息是可用的，他们是由一个单一的随机人口集体表示。假设目标是独立的，并采用“每个目标每次扫描最多一次测量”的规则，DISP过滤器传播的所有可能的轨道，与相关的可信度，基于传感器收集的测量集的序列到目前为止。几个滤波近似，旨在减少实际实现的计算成本，也进行了讨论。
This article introduces a multi-object filter for the resolution of joint detection/tracking problems involving multiple targets, derived from the novel Bayesian estimation framework for stochastic populations. Fully probabilistic in nature, the filter for Distinguishable and Independent Stochastic Populations (DISP) exploits two exclusive probabilistic representations for the potential targets. The distinguishable targets are those for which individual information is available through past detections; they are represented by individual tracks. The indistinguishable targets are those for which no individual information is available yet; they are represented collectively by a single stochastic population. Assuming that targets are independent, and adopting the "at most one measurement per scan per target" rule, the DISP filter propagates the set of all possible tracks, with associated credibility, based on the sequence of measurement sets collected by the sensor so far. A few filtering approximations, aiming at curtailing the computational cost of a practical implementation, are also discussed.