Event-Triggered Distributed Bias-Compensated Pseudolinear Information Filter for Bearings-Only Tracking Under Measurement Uncertainty

This article deals with the problem of multisensor bearings-only tracking (BOT) under measurement uncertainty. In order to effectively track the target while reducing the communication times and keeping the estimation accuracy, a novel distributed bias-compensated pseudolinear information filter with event-triggered communication mechanism and hybrid-consensus-based fusion strategy is proposed. Each sensor transmits the local information to its neighbors only when it is considered to be valuable for the fusion of its neighboring sensors based on the normalized innovation. Besides, the weight of the transmitted information, which represents the importance degree of the local estimation result, is also taken into account. The stability of the proposed algorithm is proved. Simulation results verify the effectiveness and robustness of the proposed algorithm.

Automated summary

This article discusses the problem of tracking multiple sensors' bearings-only tracking (BOT) under measurement uncertainty. To effectively track the target while reducing communication times and maintaining estimation accuracy, a novel distributed bias-compensated pseudolinear information filter with event-triggered communication mechanism and hybrid-consensus-based fusion strategy is proposed. Each sensor transmits its local information to neighboring sensors only when it is considered valuable for fusion based on normalized innovation. The stability of the proposed algorithm is proven, and simulation results demonstrate its effectiveness and robustness.