Adaptive event-triggered pseudolinear consensus filter for multi-UAVs bearings-only target tracking

Abstract

An improved version of the pseudolinear filter is proposed, namely an adaptive event-triggered instrumental variable-based pseudolinear information consensus filter, with a special focus on three-dimensional(3D) unbiased target tracking with multiple unmanned aerial vehicles (UAVs) bearings-only measurements under communication faults. To deal with the unknown communication faults, a credibility-based weighted average consensus rule is constructed, which considers the network topology and the information quality of UAVs. Furthermore, an adaptive event-triggered mechanism is designed to reduce the computation burden and communication bandwidth. The selective-angle-measurement strategy (SAMS)-based instrumental variable is integrated into the pseudolinear information consensus filter to achieve unbiased estimates. It is proved that the estimates of all UAVs will eventually achieve consistency, and its estimation errors are bounded. Finally, numerical simulations are given to verify the advantages of the proposed filtering algorithm in terms of enhanced accuracy and faster convergence rates.