Wideband multitarget passive tracking based on belief propagation theory

Abstract

In passive acoustic monitoring system, information is typically derived from radiated noise signals of targets distributed across multiple subbands. When targets are close in the node field of view, direct processing of the entire frequency band may lead to premature merging of detection results or masking of weaker targets due to differences in energy distribution among the target bands. This situation can degrade the performance of tracking algorithms because the downstream tracking processing cannot compensate for the loss of upstream measurements. This study proposes a novel wideband multitarget tracking algorithm based on belief propagation to address this challenge. Initially, a merged target model is established to simulate the mechanism of measurement generation from multiple targets. Based on this model, merged targets and each subband measurement form a cyclic association structure. This structure is processed using the loopy belief propagation algorithm to prevent the combinatorial explosion. A mixture of belief and mutual information entropy weight is established for each subband to fuse all posterior probability density functions, considering differences in subband energy distribution. Simulation and lake trial results demonstrate that this algorithm substantially improves the tracking capabilities of passive acoustic monitoring system for wideband targets, underscoring its practical application value.