Environment-independent textile fiber identification using Wi-Fi channel state information

Abstract

Textile fiber identification is a technique that can help identify the type of target textile fiber. Existing methods usually rely on expensive detection instruments, specialized researchers, and complex processing techniques. The large number of textile fibers makes it difficult for researchers to use a stable and fast method for identification. This paper introduces a textile fiber identification method based on Wi-Fi signals, and at the same time, in the actual measurement, the signal characteristics of Wi-Fi are usually interfered with by the hardware noise and multipath propagation of channel state information (CSI) measurement equipment. To eliminate the inherent noise of CSI, we designed a denoising method based on the CSI data acquisition of textile fiber samples in independent environments. Then, the features of Wi-Fi signal wavelet packet decomposition could be extracted more stably, and the principal component analysis (PCA) method was used to reduce the data dimension. Finally, the convolutional neural network (CNN) was used to classify the data features. We conducted extensive experiments to verify the effectiveness of the proposed method. The results show that the proposed method can identify all 14 kinds of common textile fibers used in the experiment, and the average accuracy is 93.25%.