A Research On Csi-Based Human Motion Detection In Complex Scenarios

A method for detecting human motion in complex scenarios based on Channel State Information (CSI) is presented. First, the sensitivity of CSI phase information to human motion is explored, especially to the strenuous motion. Through a large number of experiments, the influence of human motion on CSI phase is found out, and the characteristics of signal changes are extracted. The One-class Support Vector Machine (OSVM) in machine learning is used to detect the multi-target strenuous human motion. Line-Of-Sight (LOS) and Non-LineOf- Sight (NLOS) conditions are studied in the case of obstacles appearing in the wireless link when human motion occurred. LOS and NLOS are identified by the skewness of the channel impulse response (CIR) distribution. After identifying the LOS condition and NLOS condition in the current environment, the human motion is analyzed and detected, which further improves the accuracy of human motion detection from 70% to 91%.