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%.