Indoor Through-The-Wall Passive Target Detection Algorithm Based on Graph Convolutional Neural Network

According to variation laws of channel state information (CSI) power spectral density (PSD) in the timing series caused by different target states in indoor through-the-wall scenarios, this paper proposes a passive target detection algorithm based on graph convolutional neural (GCN). Different from the traditional correlation system for target detection based on CSI statistical features, this algorithm starts from the graph domain of CSI, constructs the GCN graph structure based on CSI time-frequency diagram, and uses the GCN that can classify the nodes in the complex graph as the classifier, which improves the performance of target detection in the indoor complex environment. Based on outlier removal and wavelet threshold denoising for original CSI information, it uses the short-time Fourier transform to obtain the time-frequency diagram of the CSI amplitude on each subcarrier. Then, according to the characteristics of each subcarrier’s CSI time-frequency diagram, the total spectrum is divided into five frequency bands on average, and the average PSD of each frequency band is calculated and sorted at every sample time. Finally, a GCN graph is constructed based on the variation law of the index of each frequency band after sorting the average PSD, and then its adjacency matrix and feature matrix are input into the GCN network for training, which can finally realize the one-to-one mapping between graph node features and target states. Experimental results show that under the scenarios of glass wall and brick wall, the proposed algorithm can essentially characterize the difference of CSI PSD change regularity caused by different target states; and its average detection accuracy is higher than that of the existing R-TTWD (Robust device-free Through-The-Wall Detection) and TWMD (The-Wall Moving Detection) target detection algorithms. © 2024 Chinese Institute of Electronics. All rights reserved.