RPCA-Based High Resolution Through-the-Wall Human Motion Detection and Classification

Radar based assisted living has received great amount of research interest in recent years. By employing the micro-Doppler features of indoor human motions, accurate recognition and classification of different types of movements become possible. Whereas, most of the existing works are focused only on free space detection, the literature on detection and recognition of human motions in through-the-wall scenarios is still in its infancy. As can be anticipated, the wall media and indoor static non-human targets would cause clutters and significantly corrupt the motion information of human subjects behind wall. However, no relevant work is reported to effectively handle this problem. In the present work, we aim to fill the gap and propose to use a low center-frequency ultra-wideband (UWB) radar system to probe the behind wall scene. Then, a Robust Principal Component Analysis (RPCA) based subspace decomposition technique, as its first reported implementation, is employed not only to remove the stationary clutters in raw range slow-time map but also to mitigate the multipath effects in the time-frequency map. Onsite experiments of detecting human motions behind a single layer of concrete wall is carried out to investigate the performance of the technique. Lastly, a two dimensional (2D)-PCA algorithm-based motion classification is provided to further verify the effectiveness of the proposed technique. Classification result shows that an enhanced recognition capability can be achieved using the proposed technique in detection and classification of indoor human motions.