Resolvable Cluster Target Tracking Based on the DBSCAN Clustering Algorithm and Labeled RFS

Abstract

When a sensor can resolve the members in a cluster, it is difficult to accurately track each target due to cooperative interaction among the targets. In this paper, we research the tracking problem of resolvable cluster targets with cooperative interaction. Firstly, we use the stochastic differential equation to model the cluster coordination rules, and the state equation of the single target in the cluster is derived. On this basis, a Bayes recursive filter tracking method based on the combination of the DBSCAN clustering algorithm and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> -GLMB filter is proposed. In the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> -GLMB filter prediction stage, the DBSCAN algorithm is used to determine the cluster where the target is located in real time. Then, the collaborative noise of the target is estimated, which will be used as the input to correct the prediction state of the target. The simulation and experiment results demonstrate the effectiveness of the proposed algorithm when the cluster is splitting, merging, and in reorganization.