Empirical Mode Decomposition (EMD) for Platform Motion Compensation in Remote Life Sensing Radar

Abstract

Radar sensing of respiratory motion from unmanned aerial vehicles (UAVs) offers great promise for remote life sensing especially in post-disaster search and rescue applications. One major challenge for this technology is the management of motion artifacts from the moving UAV platform. Prior research has focused on using an adaptive filtering approach which requires installing a secondary radar module for capturing platform motion as a noise reference. This paper investigates the potential of the empirical mode decomposition (EMD) technique for the compensation of platform motion artifacts using only primary radar measurements. Experimental results demonstrated that the proposed EMD approach can extract the fundamental frequency of the breathing motion from the combined breathing and platform motion using only one radar, with an accuracy above 87%.