BP Neural Network Based CSI Device-Free Target Classification Method

Aim at the imbalance between accuracy and expense and the heavy workload of manually extracting features in current device-free target classification systems, a channel state information (CSI) device-free target classification method based on error back propagation (BP) neural network is proposed. By extracting the CSI of the WiFi signal as the base signal and combining the neural network method with the characteristic of autonomous learning data features, the BP neural network training model is designed, which reduces the overhead caused by the manual extraction feature. Taking the height classification as an example, an experiment is carried out, and it is shown that the proposed method can distinguish four different height segments, and the average classification accuracy can reach more than 90%. © 2020, Editorial Department of Journal of Beijing University of Posts and Telecommunications. All right reserved.