A Wireless Signal Denoising Model for Human Activity Recognition

Abstract

Some pioneer WIFI signal based human activity recognition systems have been proposed. The common characteristic is to use the information of CSI(Channel State Information). Experimental results show that the extracted features of the PCA method are more obvious than that of the traditional denoising method. Even in a static environment, CSI values in Wifi signals fluctuate because WiFi devices are susceptible to surrounding electromagnetic noises. General purpose denoising methods, such as low-pass filters or mean filters, do not perform well in removing these impulse and burst noises. In this paper, we propose a method which use the low pass filter and principal component analysis simultaneously. Experimental results show that the extracted features of the PCA method are more obvious than that of the traditional methods.