Distributed multi-target tracking via consensus-based Arithmetic Average fusion

Abstract

This paper proposes a multi-target tracking fusion algorithm based on consensus arithmetic averaging to address the limitations of the sensor detection field of view and information fusion difficulty when tracking multiple targets in a distributed Airborne passive bistatic radar (APBR) network. Labelled Multi-Bernoulli (LMB) filters are executed locally when each node estimates multi-target tracks. A consensus strategy is utilised to execute the information interaction among sensors, taking into account the limited detection field of view of a single sensor. The paper proposes the LMB Consensus Arithmetic Average (LMB-CAA) fusion algorithm to address the issue of label inconsistency in multi-sensor LMB density fusion. The proposed algorithm is based on label decomposition and reconstruction and aims to avoid the influence of label mismatch on the fusion effect while simultaneously ensuring that the fusion is closed during information interaction and fusion. Simulation results demonstrate that the proposed algorithm can stably estimate multi-target tracks and improve the multi-target tracking fusion accuracy of distributed APBR networks.