Distributed multi-target tracking with labeled multi-Bernoulli filter considering efficient label matching

We propose a distributed labeled multi-Bernoulli (LMB) filter based on an efficient label matching method. Conventional distributed LMB filter fusion has the premise that the labels among local densities have already been matched. However, considering that the label space of each local posterior is independent, such a premise is not practical in many applications. To achieve distributed fusion practically, we propose an efficient label matching method derived from the divergence of arithmetic average (AA) mechanism, and subsequently label-wise LMB filter fusion is performed according to the matching results. Compared with existing label matching methods, this proposed method shows higher performance, especially in low detection probability scenarios. Moreover, to guarantee the consistency and completeness of the fusion outcome, the overall fusion procedure is designed into the following four stages: pre-fusion, label determination, posterior complement, and uniqueness check. The performance of the proposed label matching distributed LMB filter fusion is demonstrated in a challenging nonlinear bearings-only multi-target tracking (MTT) scenario.