High-Resolution Indoor Sensing Using Channel State Information of WiFi Networks

Abstract

Indoor sensing is becoming increasingly important over time as it can be effectively utilized in many applications from digital health care systems to indoor safety and security systems. In particular, implementing sensing operations using existing infrastructures improves our experience and well-being, and exhibits unique advantages. The physical layer channel state information for wireless fidelity (WiFi) communications carries rich information about scatters in the propagation environment; hence, we exploited this information to enable detailed recognition of human behaviours in this study. Comprehensive calibration and filtering techniques were developed to alleviate the redundant responses embedded in the channel state information (CSI) data due to static objects and accidental events. Accurate information on breathing rate, heartbeat and angle of arrival of the incoming signal at the receiver side was inferred from the available CSI data. The method and procedure developed can be extended for sensing or imaging the environment utilizing wireless communication networks.