Lee Eui Hyuk, Zhang Qian, Song Taek Lyul
5th Development Division, Agency for Defense Development, P.O.Box 35, Daejeon, Korea.
Department of Electronic Systems Engineering, Hanyang University, Ansan, 15588, Korea.
Sensors (Basel). 2017 Dec 10;17(12):2865. doi: 10.3390/s17122865.
A practical probabilistic data association filter is proposed for tracking multiple targets in clutter. The number of joint data association events increases combinatorially with the number of measurements and the number of targets, which may become computationally impractical for even small numbers of closely located targets in real target-tracking applications in heavily cluttered environments. In this paper, a Markov chain model is proposed to generate a set of feasible joint events (FJEs) for multiple target tracking that is used to approximate the multi-target data association probabilities and the probabilities of target existence of joint integrated probabilistic data association (JIPDA). A Markov chain with the transition probabilities obtained from the integrated probabilistic data association (IPDA) for single-target tracking is designed to generate a random sequence composed of the predetermined number of FJEs without incurring additional computational cost. The FJEs generated are adjusted for the multi-target tracking environment. A computationally tractable set of these random sequences is utilized to evaluate the track-to-measurement association probabilities such that the computational burden is substantially reduced compared to the JIPDA algorithm. By a series of simulations, the track confirmation rates and target retention statistics of the proposed algorithm are compared with the other existing algorithms including JIPDA to show the effectiveness of the proposed algorithm.
提出了一种实用的概率数据关联滤波器，用于在杂波环境中跟踪多个目标。联合数据关联事件的数量随着测量数量和目标数量的增加而呈组合增长，对于在严重杂波环境中的实际目标跟踪应用，即使是少量位置靠近的目标，这在计算上也可能变得不切实际。本文提出了一种马尔可夫链模型，用于生成一组可行的联合事件（FJEs），用于多目标跟踪，该模型用于近似多目标数据关联概率和联合集成概率数据关联（JIPDA）的目标存在概率。设计了一种马尔可夫链，其转移概率从用于单目标跟踪的集成概率数据关联（IPDA）中获得，以生成由预定数量的FJEs组成的随机序列，而不会产生额外的计算成本。针对多目标跟踪环境对生成的FJEs进行调整。利用这些随机序列的一组计算上易于处理的集合来评估航迹与测量的关联概率，从而与JIPDA算法相比，大大降低了计算负担。通过一系列仿真，将所提算法的航迹确认率和目标保留统计量与包括JIPDA在内的其他现有算法进行比较，以展示所提算法的有效性。
