Bias-Compensated Diffusion Pseudolinear Kalman Filter Algorithm for Censored Bearings-Only Target Tracking

Abstract

This letter proposes a novel bias-compensated diffusion pseudolinear Kalman filter algorithm for censored bearings-only target tracking. The proposed algorithm considers two biases caused by the censored bearing angle measurements and the correlation between the measurement vector and the pseudolinear noise. First, the inverse Mills ratio is used to rebuild the uncensored measurements, which can efficiently compensate the bias arising from the censored measurements. Then, we present a bias analysis for the censored diffusion pseudolinear Kalman filter to develop a bias compensation method, leading to the BC-DPLKF-C algorithm. Simulation results show the improved performance of the proposed Kalman filter compared with existing algorithms.