Cluster target tracking based on multi-sensor continuous-discrete PMBM filter

Abstract

Coordination effectively enhances the detection capabilities of sensors towards cluster targets. However, due to the dense distribution of cluster members and the cooperative interaction among them, continuous tracking of these targets becomes challenging. To address these issues, we propose a novel multi-sensor continuous-discrete Poisson multi-Bernoulli mixture filter, abbreviated as MS-CDPMBM-CT filter. Based on the cooperative interaction rules, we use the multivariate stochastic differential equation (SDE) to model the dynamics of cluster members in continuous time. To adapt to the Bayesian recursive process, we derive a Gaussian state transition form of the above model in the state space. In addition, by utilizing a cluster structure undirected graph and a multi-dimensional assignment (MDA) algorithm, we improve the multi-sensor continuous-discrete PMBM filter, which can iteratively estimate the posterior probability density of the target based on the continuous time state model and discrete multi-sensor measurements. During prediction, the cluster structure is exploited to provide cooperative interaction inputs that constrain member motions. During the update, the partitioning problem of multi-sensor measurements is transformed into the MDA problem for updating the global hypotheses. Simulation experiments show that the proposed method can better adapt to the cluster interaction behavior, distinguish the members within the cluster, and achieve high filtering accuracy.