Consensus-Based Labeled Multi-Bernoulli Filter With Event-Triggered Communication
Kai Shen, Chengxi Zhang, Peng Dong, Zhongliang Jing, Henry Leung
Abstract
This paper introduces a novel consensus-based labeled multi-Bernoulli (LMB) filter to tackle multi-target tracking (MTT) in a communication resource-sensitive distributed sensor network (DSN). Although consensus-based approaches provide effective tools for distributed fusion and MTT, the requirement of iterative communication makes it impractical in resource limited situations. To deal with this issue, two event-triggered strategies are proposed and incorporated into the consensus-based LMB. Focusing on the information discrepancy between the local multi-target probability density function (PDF) and the time prediction of the latest broadcast one, the integral-triggering strategy (ITS) is introduced. Furthermore, by proving that the information discrepancy (Kullback-Leibler divergence) between two LMB densities with the same label space can be decomposed into the sum of the information discrepancy of each LMB component pair (LMB components with the same label), the separated-triggering strategy (STS) is proposed. The performance of the proposed algorithms is demonstrated in a distributed multi-target tracking scenario via numerical simulations.