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

TL;DR

This paper introduces a novel adaptive filtering algorithm, RVM-RLS, that significantly improves real-time waypoint estimation accuracy for UAV terrain following during wildfire patrols, enhancing safety and wildfire detection capabilities.

Contribution

The paper presents the RVM-RLS filter guided by RVME criterion, offering a new adaptive approach for nonlinear, time-varying UAV systems in wildfire patrol scenarios.

Findings

RVM-RLS improves waypoint estimation accuracy by approximately 88% over benchmark algorithms.

The method effectively adapts to measurement noise in nonlinear, time-varying systems.

Experimental validation demonstrates practical potential for wildfire patrol applications.

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.