A Residual Variance Matching Recursive Least Squares Filter for Real-time UAV Terrain Following

TL;DR

The paper addresses real-time UAV terrain-following waypoint estimation under measurement noise in nonlinear, time-varying terrains. It introduces the Residual Variance Matching–Recursive Least Squares (RVM-RLS) filter, a dual-recursive estimator guided by the RVME principle to adapt the forgetting factor and estimate model parameters. In simulated wildfire patrol terrain, the RVM-RLS achieves up to ~88% improvements in MSE and VR over benchmark filters, demonstrating robustness to outliers and nonlinear dynamics. The work advances real-time UAV terrain-following filtering and offers practical potential for safer, more reliable wildfire surveillance and detection.

Abstract

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.

Figures (6)

• Figure 1: Longitudinal path comparison of UAV-based online terrain following in wildfire patrol: the dashed green line $A$ denotes the terrain following strategy, and the dotted red line $B$ represents the fixed altitude approach.
• Figure 2: The architecture diagram of the RVM-RLS filter
• Figure 3: The diagram of the real-time waypoints generation based on UAV-based online terrain following system.
• Figure 4: The simulation results of the Residual PolyRLS filter.
• Figure 5: Comparative simulation of estimated waypoints during online terrain following.