Indoor positioning based on channel state information and deep learning domain adaptation

Abstract

This article discusses the importance of indoor positioning technology and the challenges it faces, with a particular focus on fingerprint positioning technology based on Channel State Information (CSI). It delves into the rich channel feature information provided by CSI and its applications in indoor environments. However, over time or due to variations in the signal itself, indoor localization deployments may experience a decrease in accuracy or even become ineffective based on the initially collected data. This paper addresses the issue of decreasing model localization effectiveness due to temporal or environmental factors using knowledge from the domain adaptation area of deep learning. The aim of this paper is to minimize the domain discrepancy by reducing the distance in the feature space between the source and target domains, as well as the distance in the semantic feature centroid. As a result, it achieves an improvement of 32.70% and 33.59% over the performance degradation of the original model on the two target domains, respectively. When using only unlabeled data from the target domain, this approach outperforms three other basic deep domain adaptation methods.