Human identification plays a vital role in daily lives. A majority of biometric technologies require the active cooperation of humans, while gait recognition does not. Compared with other identification technologies, radar-based technology can monitor the human body around the clock without being affected by light/weather, and is not easy to be forged while protecting privacy. Previous researches have revealed that gait signatures acquired using radar can be used for human identification, but there is almost no literature on the long-term stability of gait signatures. Due to the long-term interval observation, the human micro-Doppler will change according to the subject (such as slight differences in walking posture). In this study, a novel network is proposed to realise stable identification of humans by extracting long-term stable features. The micro-Doppler data is processed by a short-time Fourier transform and finally classified by the proposed neural network. Data acquisition was carried out within more than a month. The experimental results demonstrate that the recognition accuracy of the validation set can reach about 99%, and the recognition accuracy of the test set can reach 90% (improved 3% compared with the network without a recurrent neural network), showing the potential of the proposed method in long-term stable identification.

中文翻译：

---

使用深度神经网络实现长期稳定的基于雷达的人体识别

身份识别在日常生活中起着至关重要的作用。大多数生物识别技术都需要人类的积极合作，而步态识别则不需要。与其他识别技术相比，基于雷达的技术可以全天候监视人体，不受光/天气的影响，并且在保护隐私的同时不易伪造。先前的研究表明，使用雷达获得的步态特征可以用于人类识别，但是几乎没有关于步态特征的长期稳定性的文献。由于长期的间隔观察，人体微多普勒仪会根据被摄对象而变化（例如，步行姿势略有不同）。在这项研究中，提出了一种新颖的网络，可以通过提取长期稳定的特征来实现对人类的稳定识别。通过短时傅立叶变换处理微多普勒数据，最后通过提出的神经网络对其进行分类。数据采集​​在一个多月内进行。实验结果表明，验证集的识别精度可以达到约99％，测试集的识别精度可以达到90％（与不使用递归神经网络的网络相比，提高了3％），显示了提出的长期稳定识别方法。