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.

翻译：在基于无人机的野火巡逻任务中，在线地形跟随的精确实时航迹点估计对于确保飞行安全和实现野火探测至关重要。然而，现有的实时滤波算法在非线性和时变系统的测量噪声下难以维持准确的航迹点，导致无人机地形跟随过程中存在飞行不稳定和野火漏检的风险。为解决这一问题，本文提出了一种基于残差方差匹配估计准则的残差方差匹配递归最小二乘滤波器，用于自适应估计非线性、时变的无人机地形跟随系统的实时航迹点。所提方法在模拟地形环境中通过基于无人机的在线地形跟随系统进行了验证。实验结果表明，与基准算法相比，RVM-RLS滤波器在多项评估指标下将航迹点估计精度提升了约88%。这些发现既体现了实时滤波方法的技术进展，也展示了RVM-RLS滤波器在无人机在线野火巡逻中的实际应用潜力。