A CNN based localization and activity recognition algorithm using multi-receiver CSI measurements and decision fusion

Abstract

With the development of the internet of things, localization and activity recognition based on WIFI signals have received much attentions. In order to improve the performance, in this paper, a convolutional neural network (CNN) based localization and activity recognition algorithm using channel state information (CSI) measurements and decision fusion is proposed. In the off-line phase, the original CSI measurements obtained from multiple receivers are preprocessed to remove the outliers and noise by Hampel filter and Gaussian filter. Then the normalized CSI measurements are rendered into RGB images. At last, at each receiver, the CNN and distributed training is used for classification learning of localization and activity recognition, respectively. In the on-line phase, the decision-level fusion is used to fuse the intermediate estimation and obtain the final localization and activity recognition results. Experiment results show the better performance of the proposed algorithm.