A Recursive Total Least Squares Solution for Bearing-Only Target Motion Analysis and Circumnavigation

TL;DR

This paper introduces a Recursive Total Least Squares method for passive bearing-only target tracking, improving accuracy and convergence in challenging nonlinear scenarios, and enhances observability through a circumnavigation controller.

Contribution

It proposes a novel RTLS algorithm for bearing-only TMA and a circumnavigation control strategy to improve system observability and estimator convergence.

Findings

RTLS outperforms pseudo-linear Kalman filter in accuracy

Circumnavigation improves estimator convergence

Method demonstrates robustness in simulations and experiments

Abstract

Bearing-only Target Motion Analysis (TMA) is a promising technique for passive tracking in various applications as a bearing angle is easy to measure. Despite its advantages, bearing-only TMA is challenging due to the nonlinearity of the bearing measurement model and the lack of range information, which impairs observability and estimator convergence. This paper addresses these issues by proposing a Recursive Total Least Squares (RTLS) method for online target localization and tracking using mobile observers. The RTLS approach, inspired by previous results on Total Least Squares (TLS), mitigates biases in position estimation and improves computational efficiency compared to pseudo-linear Kalman filter (PLKF) methods. Additionally, we propose a circumnavigation controller to enhance system observability and estimator convergence by guiding the mobile observer in orbit around the target. Extensive simulations and experiments are performed to demonstrate the effectiveness and robustness of the proposed method. The proposed algorithm is also compared with the state-of-the-art approaches, which confirms its superior performance in terms of both accuracy and stability.