提升无人机巡山时实时路径估计精度，抗干扰能力强。
A Residual Variance Matching Recursive Least Squares Filter for Real-time UAV Terrain Following
- 基于残差方差匹配准则，自适应调整滤波参数。
- 相比基准算法，路径估计精度提升约88%。
- 适合复杂地形下无人机实时巡检与火情监测。
基于无人机的实时地形跟随在森林火灾巡查任务中至关重要，准确的实时航点估计可保障飞行安全并提升火情发现能力。然而，现有实时滤波算法在非线性、时变系统中受测量噪声影响，难以保持高精度，易导致飞行不稳定或漏检火灾。为此，本文提出一种残差方差匹配递归最小二乘（RVM-RLS）滤波器，基于残差方差匹配估计（RVME）准则，自适应估计非线性、时变无人机地形跟随系统的实时航点。在模拟地形环境下的无人机在线地形跟随系统中进行了验证。实验结果表明，该方法在多个评估指标上相较基准算法航点估计精度提升约88%。研究结果展示了实时滤波方法的进展及RVM-RLS在无人机在线火灾巡查中的实用潜力。
原文摘要 · Abstract (English)
Accurate real-time waypoints estimation for the UAV-based online Terrain Following during wildfire patrol missions is critical to ensuring flight safety and enabling wildfire detection. However, existing real-time filtering algorithms struggle to maintain accurate waypoints under measurement noise in nonlinear and time-varying systems, posing risks of flight instability and missed wildfire detections during UAV-based terrain following. To address this issue, a Residual Variance Matching Recursive Least Squares (RVM-RLS) filter, guided by a Residual Variance Matching Estimation (RVME) criterion, is proposed to adaptively estimate the real-time waypoints of nonlinear, time-varying UAV-based terrain following systems. The proposed method is validated using a UAV-based online terrain following system within a simulated terrain environment. Experimental results show that the RVM-RLS filter improves waypoints estimation accuracy by approximately 88$\%$ compared with benchmark algorithms across multiple evaluation metrics. These findings demonstrate both the methodological advances in real-time filtering and the practical potential of the RVM-RLS filter for UAV-based online wildfire patrol.
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