Distributed Fusion of Highly Maneuvering Multitarget Under Limited Field of View Sensors

Abstract

The problem of tracking multiple highly maneuverable targets in a distributed sensor network is addressed under constraints of limited field of view, computational capacity, and communication resources. First, a hybrid-driven labeled multi-Bernoulli (HDLMB) filter, driven by Gaussian processes and motion models, is proposed to track multiple highly maneuverable targets. Second, the local state estimates, rather than the local multitarget posterior densities, are fused by each node. This fusion strategy decouples distributed fusion from local estimates at individual nodes, aligning better with modular applications and reducing both fusion time and communication bandwidth. Finally, a suboptimal distributed fusion algorithm based on local track matching is developed. It is designed without the prerequisite of a known sensor field of view and effectively mitigates the NP-Hard problem associated with optimal matching while tracking multiple targets by multiple sensors. Numerical experiments have demonstrated that compared to advanced distributed fusion methods, the proposed approach achieves superior tracking accuracy and incurs lower fusion costs.