Distributed Tracking of Circular Formation Targets via Graph-Theoretic Data Association

In view of the defects and omissions of the existing researches that combine group target tracking and multi-sensor information fusion, this paper proposes a novel distributed tracking algorithm for circular formation group targets. First, by combining Joint Probabilistic Data Association algorithm, a group target tracking algorithm with the circular formation group structure taken into account is presented in this paper. Next, a graph theory-based multi-sensor data association algorithm is proposed to reduce the probability of false associations and lay a foundation for subsequent multi-sensor information fusion. Furthermore, a hybrid consensus-based fusion strategy is carried out under multi-sensor networks, extending the applications of the algorithm under multi-sensor networks while guaranteeing its stability and asymptotical optimality. Simulations are presented to verify the effectiveness of the proposed algorithm.