Indoor Human Target Detection Based on WiFi and Programmable Intelligent Surface

Since it is related to personnel's activity status, indoor target detection based on WiFi is currently a research hotspot, and the signal data type is closely related to detection accuracy. However, there are few studies on the impact of signal types such as delay spread and propagation angle on detection accuracy. Therefore, this paper designs a 1-bit reconfigurable intelligent surface (RIS) and realizes the scanning of human target by regulating the indoor WiFi signal to increase the feature of received data. Seven types of characteristic data including received signal strength indication, delay spread, and angle spread in the detection space are obtained, and a data set associated with indoor human target detection is established. On this basis, the convolutional neural network (CNN) is used to classify the data, and the detection of human activity targets and room-level positioning are realized. The results show that the use of RIS to scan human targets can achieve high-precision detection with fewer monitoring nodes, which may have value for the monitoring and positioning of indoor targets.