Multiscale Residual Attention Network for Distinguishing Stationary Humans and Common Animals under Through-wall Condition using Ultra-wideband Radar

Distinguishing between humans and common animals through a wall is necessary for facilitating successful rescue of survivors and enhancing the confidence of rescuers in post-disaster search and rescue operations. However, few existing solutions are available with only dogs considered in this scenario. This poses an issue in ensuring the recognition accuracy involving different animal species. This work proposed a novel multiscale residual attention network for distinguishing between stationary humans and common animals under a through-wall condition based on ultra-wideband radar, which is yet to be performed by existing research using deep learning. Humans, dogs, cats, rabbits, and no target data are collected and distinguished. The overall architecture of the proposed method differed from conventional deep learning methods as it is constructed by parallel 3 x 3 and 5 x 5 kernels incorporated with the residual attention learning mechanism. The effect of the slow-time dimension on the classification performance is analyzed, thereby producing an optimal input size. The overall F1-score of the proposed network can reach a high value of 0.9064 and the recognition accuracy of human targets can reach 0.983 satisfying the requirements for post-disaster rescue. Then, the effectiveness and advancement of the three components of the overall network architecture are validated by ablation studies. Finally, the proposed method is compared with three state-of-the-art methods. Comparison results indicate that the proposed method achieve a better performance. The network and its results are envisioned to be applied in various practical situations, such as earthquake rescue and intelligent homecare.