With the development of the Internet of Things (IoT), many kinds of wireless signals (e.g., Wi-Fi, LoRa, RFID) are filling our living and working spaces nowadays. Beyond communication, wireless signals can sense the status of surrounding objects, known as wireless sensing , with their reflection, scattering, and refraction while propagating in space. In the last decade, many sophisticated wireless sensing techniques and systems were widely studied for various applications (e.g., gesture recognition, localization, and object imaging). Recently, deep Artificial Intelligence (AI), also known as Deep Learning (DL), has shown great success in computer vision. And some works have initially proved that deep AI can benefit wireless sensing as well, leading to a brand-new step toward ubiquitous sensing. In this survey, we focus on the evolution of wireless sensing enhanced by deep AI techniques. We first present a general workflow of Wireless Sensing Systems (WSSs) which consists of signal pre-processing, high-level feature, and sensing model formulation. For each module, existing deep AI-based techniques are summarized, further compared with traditional approaches. Then, we provide a view of issues and challenges induced by combining deep AI and wireless sensing together. Finally, we discuss the future trends of deep AI to enable ubiquitous wireless sensing.

中文翻译：

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支持深度 AI 的无处不在的无线传感

随着物联网（IoT）的发展，多种无线信号（如Wi-Fi、LoRa、RFID）充斥着当今我们的生活和工作空间。除了通信之外，无线信号还可以感知周围物体的状态，称为无线传感，它们在空间传播时的反射、散射和折射。在过去的十年中，许多复杂的无线传感技术和系统被广泛研究用于各种应用（例如，手势识别、定位和对象成像）。最近，深度人工智能 (AI)，也称为深度学习 (DL)，在计算机视觉领域取得了巨大成功。一些工作初步证明，深度人工智能也可以使无线传感受益，从而向无处不在的传感迈出了全新的一步。在本次调查中，我们重点关注由深度 AI 技术增强的无线传感的演变。我们首先介绍了无线传感系统 (WSS) 的一般工作流程，其中包括信号预处理、高级特征和传感模型制定。对于每个模块，总结了现有的基于深度 AI 的技术，进一步与传统方法相比。然后，我们提供了将深度 AI 和无线传感结合在一起所引发的问题和挑战的观点。最后，我们讨论了深度 AI 的未来趋势，以实现无处不在的无线传感。