Abstract
The radar penetrating technique has aroused a keen interest in the research community, due to its superior abilities for through-the-wall indoor human motion monitoring. Micro-Doppler signatures in this situation play a significant role in recognition and classification for human activities. However, the live wire buried in the wall introduces additive clutters to the spectrograms. Such degraded spectrograms drastically affect the performance of behind-the-wall human activity detection. In this paper, an ultra-wideband (UWB) radar system is utilized in the through-the-wall scenario to get the feature enhanced micro-Doppler signature called range-max time-frequency representation (R-max TFR). Then, a recently introduced Cycle-Consistent Generative Adversarial Network (Cycle GAN) is employed to realize the end-to-end de-wiring task. Cycle GAN can learn the mapping between spectrograms with and without the live wire effect. To minimize the wiring clutters, a loss function called identity loss is introduced in this work. Finally, the proposed de-wiring approach is evaluated through classification. The results show that the proposed Cycle GAN architecture outperforms other state-of-art de-wiring methods.
Highlights
Radar based indoor human activity detection has attracted considerable attention from researchers and practitioners due to its outstanding penetration capabilities and robustness against different deployment and illumination conditions [1]
To preserve the most authentic human motion micro-Doppler signatures between X and Y, we introduce an additional identity mapping loss function [22], which was first employed in Domain Transfer Network (DTN) to encourage the mapping to keep the color composition of the output as the same as that of the input
The classification results of the aforementioned de-wiring methods are presented, to verify that the novel Cycle GAN with identity loss could achieve the best performance in terms of the classification accuracy of the nine types of the live wire corrupted human motions
Summary
Radar based indoor human activity detection has attracted considerable attention from researchers and practitioners due to its outstanding penetration capabilities and robustness against different deployment and illumination conditions [1]. In [11], a conditional generative adversarial network (cGAN) [18] was applied to learn the mapping from the live wire corrupted spectrograms to the wiring effect free ones. The network can learn the translation between the live wire corrupted and wire clutter free spectrograms in an unsupervised manner without using paired training samples [19]. To preserve the most authentic human motion micro-Doppler signatures between X and Y, we introduce an additional identity mapping loss function [22], which was first employed in Domain Transfer Network (DTN) to encourage the mapping to keep the color composition of the output as the same as that of the input. (a) We utilize a Cycle GAN-based optimization framework to meet the challenging micro-Doppler signature de-wiring problem without using the complicated wire effect modeling methods.
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