WALL CLUTTER SUPPRESSION FOR THROUGH-WALL RADAR ON HOVERING DRONE

Moving targets detection of through-wall radar (TWR) has attracted a significant amount of research interest in recent years. Most previous studies of moving target detection mainly focused on stationary platforms, like hand-held TWR. Moving-human target information can be detected on the echo map by moving target indicator (MTI). The combination of drones and TWR improves the detection area of radar. However, the TWR on hovering drones is unstable, which causes wall-scattered clutter submerging the moving human signal. This paper employed a wall clutter suppression technique based on robust principal component analysis (RPCA) to remove the stationary clutter of TWR. We first give the signal model of linear frequency modulation continuous wave (LFMCW) radar. Then, an RPCA algorithm is introduced to attenuate wall clusters. Finally, a field experiment of TWR on a hovering drone was performed and validated the effect of RPCA on UAV-borne TWR data. This paper approved that moving human echoes can be detected in drone TWR echoes.