TRACKING IN A CLUTTERED ENVIRONMENT WITH PROBABILISTIC DATA ASSOCIATION
TRACKING IN A CLUTTERED ENVIRONMENT WITH PROBABILISTIC DATA ASSOCIATION
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DOI：
10.1016/0005-1098(75)90021-7
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发表时间：
1975-01-01
期刊：
影响因子：
6.4
通讯作者：
TSE, E
中科院分区：
文献类型：
--
作者：
BARSHALOM, Y;TSE, E
针对测量数据来源不确定时的跟踪问题，提出了一种新的跟踪方法。假设一个感兴趣的对象（“目标”）在轨道上，并且在目标返回的预测位置附近的某个时间检测并解决了许多不希望的返回。提出了一种次优估计程序，该程序考虑了所有可能来自跟踪对象的测量，但没有增长的内存和计算需求。每次返回的概率（位于预测返回的某个邻域内，称为“验证区域”）是正确的，这被称为“概率数据关联”（PDA）。假定非期望收益均匀且独立分布。估计是通过使用PDA方法和适当修改的跟踪滤波器（称为PDAF）来完成的。由于PDAF的计算量要求仅略高于标准滤波器，因此该方法可用于实时系统。在混乱环境中跟踪目标的仿真结果表明，PDAF比目前用于这类问题的标准滤波器提供了明显更好的结果。
This paper presents a new approach to the problem of tracking when the source of the measurement data is uncertain. It is assumed that one object of interest (‘target’) is in track and a number of undesired returns are detected and resolved at a certain time in the neighbourhood of the predicted location of the target's return. A suboptimal estimation procedure that takes into account all the measurements that might have originated from the object in track but does not have growing memory and computational requirements is presented. The probability of each return (lying in a certain neighborhood of the predicted return, called ‘validation region’) being correct is obtained—this is called ‘probabilistic data association’ (PDA). The undesired returns are assumed uniformly and independently distributed. The estimation is done by using the PDA method with an appropriately modified tracking filter, called PDAF. Since the computational requirements of the PDAF are only slightly higher than those of the standard filter, the method can be useful for real-time systems. Simulation results obtained for tracking an object in a cluttered environment show the PDAF to give significantly better results than the standard filter currently in use for this type of problem.