Auxiliary truncated particle filtering with least-square method for bearings-only maneuvering target tracking

Abstract

In the paper, a novel auxiliary truncated particle filtering for bearings-only maneuvering target tracking (ATPF-BOT) is proposed. In the proposed algorithm, a modified prior probability density function (PDF) is derived to solve the modeling uncertainty problem, which can simultaneously incorporate current measurement information and target characteristic information. Meanwhile, the proposal distribution is jointly designed by using the prior PDF and the modified prior PDF. Moreover, the proposal distribution is approximately calculated based on adaptive least square method so as to apply the ATPF algorithm for bearings-only maneuvering target tracking, and a practical algorithm is also developed. The experiment results show that the proposed algorithm is computationally efficient and successfully implemented in bearings-only target tracking systems.