ResLoc: Deep residual sharing learning for indoor localization with CSI tensors

Abstract

Wi-Fi based indoor localization has attracted great interest due to its ubiquitous access in many indoor environments. In this paper, we propose ResLoc, a deep residual sharing learning based system for indoor localization with channel state information (CSI) tensor data. We first introduce CSI data in wireless systems and show how to build CSI tensors for indoor localization. Then, we present the design of ResLoc, which employs dual-channel, bi-modal CSI tensor data to train the deep network using the proposed deep residual sharing learning in the offline phase. In the online test phase, we use newly received CSI tensor data to estimate the location of the mobile device based on an enhanced probabilistic method. The experimental results show that the proposed ResLoc system can obtain submeter level accuracy with a single access point.