Distributed Multiple Resolvable Group Targets Tracking Based on Hypergraph Matching

Abstract

This paper proposes a solution of distributed multiple resolvable group targets tracking (DMRGTT) by exploring the labeled multi-Bernoulli (LMB) filter and distributed fusion rules including generalized covariance intersection (GCI) and weighted arithmetic average (WAA). Due to the proximity of targets’ positions within the same group and the label mismatching problem for the fusion between LMB densities, only utilizing the similarity or distance between targets from different sensors cannot return a satisfactory solution of label matching (LM) results. To cope with this problem, the hypergraph matching (HM) theory is adopted to assist in finding the matched labels. Specifically, the hyperedge-to-hyperedge (H2H) matching matrix is first constructed and a vertex-to-vertex (V2V) matrix is returned. Then, the V2V matrix is further combined with the traditional distance matrix to construct a joint cost matrix. Next, substitute the joint cost matrix into the Hungarian algorithm and the LM results are obtained. Lastly, pairwise GCI-LMB and WAA-LMB fusion processes are performed based on the obtained LM results. Experiment results reveal the effectiveness and robustness of the proposed fusion approaches.