Event-based distributed bias compensation pseudomeasurement information filter for 3D bearing-only target tracking

Abstract

This paper investigates the distributed 3D bearing-only target tracking problem in UAV network. Because of the limited computation capability and constrained power supply of UAV, traditional time-triggered nonlinear filters would inevitably cause serious network congestions. Thus, we proposed an efficient yet effective algorithm named event-based distributed pseudomeasurement information filter (EB-PMIF) by combining the deterministic Send-on-Delta triggering event-based mechanism and pseudomeasurement information filter in order to reduce the computation load and communication requirements. Since the correlated pseudomeasurement matrix and pseudomeasurement noise in EB-PMIF cause bias estimation, we analyzed the root cause of the bias and proposed a bias compensation filter named event-based distributed bias compensation pseudomeasurement information filter (EB-PMIF-BC) to eliminate the bias and improve the estimation accuracy. The proposed EB-PMIF and EB-PMIF-BC are demonstrated with an illustrative 3D bearing-only target tracking example. Simulation results verify the efficiency and effectiveness of the proposed methods.