Optimizing distributed multi-sensor multi-target tracking algorithm based on labeled multi-bernoulli filter

In this paper, we propose an improved distributed fusion algorithm under the Labeled multi-Bernoulli (LMB) filter framework. Firstly, the LMB parameter set is augmented by a new group variable, which is able to record the matching information of the neighbour sensors. Then the matching LMB components of the survival targets between the sensors can be fused directly by checking the group variable, greatly reducing the time cost of the matching calculation and the interference from the newborn targets. While for the newborn targets, the Murty algorithm is employed and only performed once to find the best matching relation between the sensors. Finally, experimental results show that the proposed algorithm offers a better tracking performance than the state-of-the-art R-GCI-LMB algorithm with lower computational complexity and higher tracking accuracy.