Multiscale Residual Attention Network for Distinguishing Stationary Humans and Common Animals Under Through-Wall Condition Using Ultra-Wideband Radar

Abstract

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 3×3 and 5×5 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. INDEX TERMS convolutional neural network (CNN), distinguishing between stationary humans and common animals, post-disaster rescue, residual attention learning mechanism, ultra-wideband (UWB) radar.

Multiple Stationary Human Targets Detection in Through-Wall UWB Radar Based on Convolutional Neural Network

“…UWB radar is widely used in ground-penetrating radar (GPR), through-wall imaging, post-earthquake rescue, and so on, due to its high range resolution and high penetration capability [1][2][3][4][5][6][7]. The frequently benefitted waveforms in UWB radar literature are short impulses [8][9][10][11], pseudo-random coded signals [12][13][14][15], frequencymodulated continuous wave (FMCW) signals [16][17][18][19][20], and stepped-frequency continuous wave (SFCW) signals [21][22][23][24]. UWB impulse radar has been increasingly adopted by researchers for human target detection due to its simple structure, low-cost, and low power consumption [25][26][27].…”

Research on Identifying Life States by Analyzing Physiological Raw of Rabbits under Typical Post-disaster Rescues

“…Bao et al [11] designed a cascaded spatialtemporal three-stage detector to accurately detect short-range targets and remove a few false alarms. In [12] and [13], two novel methods using support vector machine and convolution neural networks respectively are proposed to distinguish stationary human and common animals under through-wall condition. Common animals include some family pets such as dogs, cats, and rabbits.…”

Ultra-Wideband Radar-Based Indoor Activity Monitoring for Elderly Care

“…In this case, UWB signal detection is based on analysing the identifiable Doppler shift patterns caused by the different gaits. In [ 13 ], deep learning is utilized in data postprocessing to help distinguish humans, three different animals, and empty room situations using through-the-wall UWB radar. The authors mentioned that the proposed technology can also be used in rescue applications, e.g., after earthquakes, to localize buried persons.…”

“…Not only applicable in residency environments, UWB radar is a feasible solution in rescue and other safety related operations, as presented in [ 13 , 22 ]. The dual use of analysis of the received signal can be utilized to observe and locate living persons inside ruins, an avalanche, etc.…”