Du Haocui, Xie Weixin
Automatic Target Recognition (ATR) Key Laboratory, Shenzhen University, Shenzhen 518060, China.
Sensors (Basel). 2020 Sep 20;20(18):5387. doi: 10.3390/s20185387.
The existence of clutter, unknown measurement sources, unknown number of targets, and undetected probability are problems for multi-extended target tracking, to address these problems; this paper proposes a gamma-Gaussian-inverse Wishart (GGIW) implementation of a marginal distribution Poisson multi-Bernoulli mixture (MD-PMBM) filter. Unlike existing multiple extended target tracking filters, the GGIW-MD-PMBM filter computes the marginal distribution (MD) and the existence probability of each target, which can shorten the computing time while maintaining good tracking results. The simulation results confirm the validity and reliability of the GGIW-MD-PMBM filter.
杂波的存在、未知测量源、目标数量未知以及未检测概率是多扩展目标跟踪面临的问题，为了解决这些问题，本文提出了一种基于伽马-高斯-逆威沙特（GGIW）的边际分布泊松多伯努利混合（MD-PMBM）滤波器实现方法。与现有的多扩展目标跟踪滤波器不同，GGIW-MD-PMBM滤波器计算每个目标的边际分布（MD）和存在概率，这可以在保持良好跟踪结果的同时缩短计算时间。仿真结果证实了GGIW-MD-PMBM滤波器的有效性和可靠性。
