用递归总最小二乘法提升无源测向目标跟踪精度与稳定性
A Recursive Total Least Squares Solution for Bearing-Only Target Motion Analysis and Circumnavigation
- 基于总最小二乘思想设计递归算法，减少位置估计偏差
- 相比伪线性卡尔曼滤波，计算效率更高且收敛更稳
- 结合环绕控制器增强可观测性，适合移动观测平台应用
无源测向目标运动分析（Bearing-only TMA）在多种场景中具有潜力，因其方位角易于测量。然而，由于方位测量模型的非线性及缺乏距离信息，导致可观测性差、估计算法难收敛。本文提出一种递归总最小二乘（RTLS）方法，用于移动观测器在线定位与跟踪目标。该方法借鉴总最小二乘思想，有效缓解位置估计偏差，并比伪线性卡尔曼滤波（PLKF）更具计算效率。此外，提出一种环绕控制策略，引导移动观测器绕目标运行，以增强系统可观测性并加快估计算法收敛。通过大量仿真与实验验证了所提方法的有效性与鲁棒性。与当前先进方法对比，结果表明其在精度与稳定性方面均表现更优。
原文摘要 · Abstract (English)
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.
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