Physics-Aware Design of Multi-Branch GAN for Human RF Micro-Doppler Signature Synthesis
Physics-Aware Design of Multi-Branch GAN for Human RF Micro-Doppler Signature Synthesis
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DOI：
10.1109/radarconf2147009.2021.9455194
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
M. M. Rahman-M.;S. Gurbuz;M. Amin
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文献类型：
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作者：
M. M. Rahman-M.;S. Gurbuz;M. Amin
最近提出了生成对抗网络（GAN）用于合成RF微多普勒特征，以减轻低样本支持的问题，并能够训练更深的神经网络（DNN）以改进RF信号分类。然而，当应用于人体微多普勒特征进行步态分析时，GAN会受到系统运动学差异的影响，从而降低性能。针对这一问题，本文提出了一种物理感知的损失函数和多分支GAN结构的设计。我们的研究结果表明，使用所提出的方法合成的RF步态签名具有更大的相关性和相似性测量RF步态签名，同时也提高了分类五种不同步态的准确性。
Generative adversarial networks (GANs) have been recently proposed for the synthesis of RF micro-Doppler signatures to mitigate the problem of low sample support and enable the training of deeper neural networks (DNNs) for improved RF signal classification. However, when applied to human micro-Doppler signatures for gait analysis, GANs suffer from systemic kinematic discrepancies that degrade performance. As a solution to this problem, this paper proposes the design of a physics-aware loss function and multi-branch GAN architecture. Our results show that RF gait signatures synthesized using the proposed approached have greater correlation and similarity to measured RF gait signatures, while also improving the accuracy in classifying five different gaits.