Multi-target tracking using joint probabilistic data association
Multi-target tracking using joint probabilistic data association
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DOI：
10.1109/cdc.1980.271915
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发表时间：
1980-12
期刊：
影响因子：
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通讯作者：
T. Fortmann;Y. Bar-Shalom;M. Scheffe
中科院分区：
文献类型：
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作者：
T. Fortmann;Y. Bar-Shalom;M. Scheffe
概率数据关联（ProbabilityDataAssociation，PDA）方法是在假设只有一个真实的目标存在的情况下，通过计算验证门中每个候选测量值的后验概率来实现的。本文给出了泊松杂波中多目标联合后验概率的联合概率数据关联（JPDA）算法的一些新的理论结果。将该算法应用于多传感器和目标的被动声纳跟踪问题，其中目标不能从单个传感器完全观测。目标被建模为四个地理状态，两个或更多的声学状态，和现实（即低）的检测概率在每个采样时间。两个严重干扰的目标的仿真结果，这些说明了通过计算联合概率所获得的显着改善。
The Probabilistic Data Association (PDA) method, which is based on computing the posterior probability of each candidate measurement found in a validation gate, assumes that only one real target is present and all other measurements are Poisson-distributed clutter. In this paper, some new theoretical results are presented on the Joint Probabilistic Data Association (JPDA) algorithm, in which joint posterior probabilities are computed for multiple targets in Poisson clutter. The algorithm is applied to a passive sonar tracking problem wlth multiple sensors and targets, in which a target is not fully observable from a single sensor. Targets are modeled with four geographic states, two or more acoustic states, and realistic (i.e. low) probabilities of detection at each sample time. Simulation results are presented for two heavily interfering targets; these illustrate the dramatic improvements obtained by computing joint probabilities.