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The Cardinality Balanced MeMBer (CBMeMBer) filter is a single sensor multi-target tracking method based on the random finite set. Compared with the single sensor system, the multi-sensor system can achieve more stable and better performance in tracking targets. However, some problems exist in multi-sensor system based on the CBMeMBer filter. Tracks in the CBMeMBer filter are described by parameter sets which may be generated by miss-detection, targets and clutters. It is difficult to associate the parameter sets correctly because of their complex forms and various types. Moreover, the filter reacts slowly to disappeared targets, which leads to a cardinality overestimation. This problem may be more serious in the multi-sensor system. To deal with the above problems, the parameter sets association and fusion methods are presented in this paper. By three association processes with the adaptive thresholds selection approaches, parameter sets corresponding to the same target are grouped into one parameter set partition. Parameter sets have different association thresholds because of their different accuracies. The fusion method considers the types and relationships of parameter sets in the partition simultaneously and uses a joint credibility to accelerate changes in existence probability. The cardinality estimation decreases rapidly when the target disappears. The theoretical analysis and experiment results of different tracking scenarios show that the proposed methods perform well in both state estimation and cardinality estimation.

Multi-Target Tracking by Associating and Fusing the Multi-Bernoulli Parameter Sets