| dc.description.abstract | In recent years, with the rapid development of the Internet of Things (IoT), Wi-Fi devices have become ubiquitous. Wi-Fi signals have become a popular research topic in the field of detection, recognition, and sensing. Research related to these topics increase rapidly. Under the Wi-Fi standard, Orthogonal Frequency-Division Multiplexing (OFDM), combined with Multiple-Input Multiple-Output (MIMO) systems, generates rich and complex Channel State Information (CSI). This reflects the signal′s propagation from the transmitter to the receiver in space, influenced by multipath effects such as scattering, fading, and reflection. Any activity in an indoor environment will cause changes in the CSI. The characteristics and rich variations of CSI, as well as advantages like non-contact sensing, wide sensing range, privacy protection, and low difficulty in obtaining equipment, make Wi-Fi signals the preferred choice for environmental sensing. However, the CSI itself is affected by the multipath effect, has high noise and high complexity characteristics, and it is difficult to extract useful information directly from the signal. Additionally, the OFDM system used in the Wi-Fi standard causes phase shifts in CSI. Therefore, phase correction and signal feature extraction are the key steps in applying CSI. This study, inspired by the CSI model, analyzes the impact of the OFDM system and the multipath communication model on CSI. Use different calibration equations to reduce the phase offset caused by OFDM, explores the differences in amplitude and phase performance under multipath effects in a simulated experimental environment and finally demonstrates the feasibility of CSI in practical applications. | en_US |