Liu Yang, Wang Tiexing, Jiang Yuexin, Chen Biao
IEEE Trans Neural Netw Learn Syst. 2022 Apr;33(4):1571-1583. doi: 10.1109/TNNLS.2020.3042908. Epub 2022 Apr 4.
This article explores the use of ambient radio frequency (RF) signals for human presence detection through deep learning. Using Wi-Fi signal as an example, we demonstrate that the channel state information (CSI) obtained at the receiver contains rich information about the propagation environment. Through judicious preprocessing of the estimated CSI followed by deep learning, reliable presence detection can be achieved. Several challenges in passive RF sensing are addressed. With presence detection, how to collect training data with human presence can have a significant impact on the performance. This is in contrast to activity detection when a specific motion pattern is of interest. A second challenge is that RF signals are complex-valued. Handling complex-valued input in deep learning requires careful data representation and network architecture design. Finally, human presence affects CSI variation along multiple dimensions; such variation, however, is often masked by system impediments, such as timing or frequency offset. Addressing these challenges, the proposed learning system uses preprocessing to preserve human motion-induced channel variation while insulating against other impairments. A convolutional neural network (CNN) properly trained with both magnitude and phase information is then designed to achieve reliable presence detection. Extensive experiments are conducted. Using off-the-shelf Wi-Fi devices, the proposed deep-learning-based RF sensing achieves near-perfect presence detection during multiple extended periods of test and exhibits superior performance compared with leading edge passive infrared sensors. A comparison with existing RF-based human presence detection also demonstrates its robustness in performance, especially when deployed in a completely new environment. The learning-based passive RF sensing thus provides a viable and promising alternative for presence or occupancy detection.
本文探讨了如何通过深度学习利用环境射频（RF）信号进行人体存在检测。以Wi-Fi信号为例，我们证明了在接收器处获得的信道状态信息（CSI）包含有关传播环境的丰富信息。通过对估计的CSI进行明智的预处理，然后进行深度学习，可以实现可靠的存在检测。文中还讨论了被动射频传感中的几个挑战。对于存在检测而言，如何收集有人存在时的训练数据会对性能产生重大影响。这与关注特定运动模式时的活动检测形成对比。第二个挑战是射频信号是复数值的。在深度学习中处理复数值输入需要仔细的数据表示和网络架构设计。最后，人体存在会影响CSI在多个维度上的变化；然而，这种变化常常被诸如定时或频率偏移等系统障碍所掩盖。针对这些挑战，所提出的学习系统使用预处理来保留人体运动引起的信道变化，同时抵御其他损伤。然后设计一个经过幅度和相位信息适当训练的卷积神经网络（CNN），以实现可靠的存在检测。文中进行了大量实验。使用现成的Wi-Fi设备，所提出的基于深度学习的射频传感在多个延长的测试期间实现了近乎完美的存在检测，并且与前沿的被动红外传感器相比表现出卓越的性能。与现有的基于射频的人体存在检测进行比较也证明了其在性能上的稳健性，特别是在部署到全新环境中时。因此，基于学习的被动射频传感为存在或占用检测提供了一种可行且有前景的替代方案。
