An Adaptive Filter Technique for Platform Motion Compensation in Unmanned Aerial Vehicle Based Remote Life Sensing Radar

Abstract

Unmanned Aerial Vehicles (UAVs) have demonstrated efficacy as a platform for remote life sensing in post-disaster search and rescue applications. Radar-assisted UAV respiration motion sensing technology also shows promise yet a significant technological challenge remains associated with interfering motion artefacts from the moving UAV platform. The feasibility of integrating an adaptive filter approach for the compensation of platform motion artefacts is investigated here for the extraction of respiratory motion signatures. A 24-GHz dual radar system was attached to a mechanical mover to emulating motion artefacts while measuring the motion of a robotic breathing phantom designed to reproduce breathing motion patterns. Recursive least square (RLS) and a least mean square (LMS) adaptive filter algorithms were employed to test efficacy for extracting respiratory rate from the motion corrupted breathing signal. Experimental results demonstrated that the RLS performed best with an accuracy of 98.24% for extracting the frequency of the robotic breathing phantom mover. The proposed system has several potential applications including military, humanitarian, and post-disaster search and rescue operations.