Range-Max Enhanced Ultrawideband Micro-Doppler Signatures of Behind-the-Wall Indoor Human Motions

In this article, an ultrawideband (UWB) radar is first employed to probe through the opaque wall media to detect behind-the-wall human motions. By employing such a radar, a high-resolution time-range map with different body parts' reflections highly discriminable in range direction can be obtained. Second, a high-pass filter is applied to remove the wall effects in the raw time-range map. Then, with the aim of exploiting the rich range information so as to enhance their corresponding micro-Doppler features, a novel range-max enhancement strategy is proposed to extract the most significant micro-Doppler feature of each time-frequency cell along range direction for a specific motion. Finally, the effectiveness of the proposed motion feature enhancement strategy is investigated by means of onsite experiments. Comparative classifications using different convolutional neural network (CNN) structures show that the proposed approach outperforms other state-of-the-art micro-Doppler feature extraction methods. The comparison with the narrowband detection case also proves its superiority in feature enhancement in the narrowband detection scene.