Convolutional neural networks based indoor Wi-Fi localization with a novel kind of CSI images

Indoor Wi-Fi localization of mobile devices plays a more and more important role along with the rapid growth of location-based services and Wi-Fi mobile devices. In this paper, a new method of constructing the channel state information (CSI) image is proposed to improve the localization accuracy. Compared with previous methods of constructing the CSI image, the new kind of CSI image proposed is able to contain more channel information such as the angle of arrival (AoA), the time of arrival (TOA) and the amplitude. We construct three gray images by using phase differences of different antennas and amplitudes of different subcarriers of one antenna, and then merge them to form one RGB image. The localization method has off-line stage and on-line stage. In the off-line stage, the composed three-channel RGB images at training locations are used to train a convolutional neural network (CNN) which has been proved to be efficient in image recognition. In the on-line stage, images at test locations are fed to the well-trained CNN model and the localization result is the weighted mean value with highest output values. The performance of the proposed method is verified with extensive experiments in the representative indoor environment.