Depthwise Separable Convolution based Passive Indoor Localization using CSI Fingerprint

Abstract

Wi-Fi-based indoor localization has received extensive attention in the academic community. However, most WiFi-based indoor localization systems have complex models and high localization delays, which limit the universality of these localization methods. To solve these problems, we propose a depthwise separable convolution based passive indoor localization system (DSCP) using Wi-Fi channel state information (CSI). DSCP is a fingerprint-based localization system, which includes an offline training phase and an online localization phase. In the offline training phase, the indoor scenario is first divided into different areas to set training locations for collecting CSI. Then amplitude differences of these CSI subcarriers are extracted for constructing location fingerprints, thereby training the CNN. In the online localization phase, CSI data is first collected at the test locations, then the location fingerprint is extracted and finally fed to the trained network to obtain the predicted location. The experimental results show that DSCP has a short training time and a low localization delay. DSCP achieves a high localization accuracy, upper than 97%, and a small median localization distance error of 0.98 m in open indoor scenarios.