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

2025-12-05

AI Summary

In unmanned aerial vehicle (UAV) wildfire surveillance, low waypoint estimation accuracy under nonlinear, time-varying environmental conditions leads to flight instability and missed fire detection. To address this, we propose a residual variance matching recursive least squares (RVM-RLS) filtering method. Our approach innovatively introduces a residual variance matching criterion to enable online, adaptive identification of system noise statistics, thereby significantly enhancing the filter’s robustness and estimation accuracy during dynamic terrain-following missions. Evaluated in a simulated wildfire terrain-following system, the proposed RVM-RLS achieves an 88% improvement in average waypoint estimation accuracy over conventional filtering algorithms. Moreover, it demonstrates substantial gains across multiple performance metrics—including faster convergence, reduced steady-state estimation error, and superior resilience to environmental disturbances—validating its effectiveness for real-time, safety-critical UAV wildfire monitoring.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Estimates real-time UAV waypoints for terrain following
• Addresses measurement noise in nonlinear, time-varying systems
• Improves accuracy for wildfire patrol flight safety

Innovation

• RVM-RLS filter adaptively estimates waypoints
• Residual Variance Matching guides adaptive filtering
• Improves accuracy by 88% in simulations