As an emerging technology, WiFi sensing has garnered widespread attention in recent years. However, due to limitations in WiFi bandwidth and hardware capacity, multi-target sensing with WiFi remains an unresolved issue. In this paper, we presentSlingShot, a first-of-its-kind approach for multi-target WiFi sensing enabled by metasurface.SlingShotexploits a metasurface's ability of beam scanning to periodically scan among targets in a high frequency, so their sensing signals can be separated in the time domain. However, the WiFi-initiated and the metasurface-initiated signals will interfere with each other, which could significantly degrade the sensing accuracy. Moreover, the WiFi devices and the metasurface operate in a distributed manner, so the inherent clock offset and clock drift among them make it difficult to accurately separate the sensing signals of the targets. To address the above challenges,SlingShotleverages the metasurface's ability of phase shifting to cancel interfering signals and designs a passive clock synchronization scheme to synchronize the metasurface and WiFi devices. We implementSlingShotand evaluate it under various settings. Results show thatSlingShotcan sense up to 8 targets. Respectively compared to the state-of-the-art methods,SlingShothas 6.75 % higher mean accuracy in activity recognition and 58.34 % lower mean error in respiration monitoring.

Reconfigurable intelligent surface (RIS) has emerged as a promising technology for wireless communication systems due to its ability to manipulate electromagnetic waves. With advantages such as low hardware complexity and low power consumption, RIS shows significant potential in positioning applications. This paper presents an RIS-based wireless signal sensing method that operates under the constraint of passive reflection while leveraging the space-time coding capabilities of RIS. By applying a space-time coding matrix on the RIS, the beamspace domain and the angle of arrival (AoA) of signals incident on the RIS can be efficiently estimated, requiring only the processing of single-channel received signals at the access point. Building upon this, a positioning prototype system utilizing two 27 GHz millimeter-wave RIS panels is developed and implemented, supporting real-time user positioning. Experimental results demonstrate that the prototype system achieves centimeter-level positioning accuracy, with errors below 10 cm in 97.22% of measurement cases, thereby validating the effectiveness of the proposed sensing and positioning scheme. These findings may pave the way for further exploration of RIS-based integration of sensing and communication technologies.

The rapid development of wireless sensing technology has opened new possibilities for non-contact health monitoring. Among them, respiration is crucial information for assessing vital signs. However, traditional methods face challenges of signal interference and overlap in multi-person environments. In this work, we propose a novel method for multi-person respiration detection using a digital programmable metasurface (DPM). This method takes advantage of the modulation characteristics of the DPM in the time and space domain. It divides the Channel State Information (CSI) from the Wi-Fi transmitter into multiple sub-signals in the time domain and modulates the radiation directions of the sub-signals for space redistribution. These signals are received by the Wi-Fi receiver and recombined to restore the CSI in each direction, thus enabling the accurate extraction of respiration signals from targets in different directions. Experimental results show that this method can directionally sense the human respiration information in a specific direction under the static working mode. Under dynamic scanning mode, it can effectively separate and detect the respiration information of four people from different directions. This system has great potential for applications in wireless communication, healthcare, and smart home environments.

Direction of Arrival (DOA) estimation is essential in building the wireless electromagnetic (EM) environment. However, the conventional DOA estimation methods have been criticized for their hardware complexity, high cost, and low energy efficiency. Recently, metasurface-based methods have emerged as promising alternatives that offer cost-effective solutions. Nevertheless, the existing studies have neglected angle sensitivity, an intrinsic physical property of metasurface, leading to errors in DOA estimations. To address this issue, we propose a generalized high-precision and wide-angle DOA estimation method based on the space-time-coding digital metasurfaces (STCDM). By carefully designing two elementary STC sequences, we realize two angle-independent harmonic states to construct a specified orthogonal matrix. Then, the harmonics, modulated by each meta-column in accordance with the matrix, are sampled by a single channel receiver at different time intervals. Upon the reception of the signal, the information on the metasurface can be comprehensively retrieved through the matrix operations, facilitating the high-accuracy DOA estimations using the array signal processing (ASP) algorithms. This work focuses on eliminating the influence of angle sensitivity on the metasurface-based DOA estimations, which guarantees high accuracy and substantially reduces hardware requirements. The proposed method holds potential for various applications, including Internet of Things (IoT) and integrated sensing and communication (ISAC).