Smartphone Sensor Fusion For Indoor Localization: A Deep LSTM Approach

Abstract

With the fast increasing demands of location-based service and the proliferation of smartphones and other mobile devices, accurate indoor localization has attracted great interest. In this thesis, we present DeepML, a deep long short-term memory (LSTM) based system for indoor localization using smartphone magnetic and light sensors. We verify the feasibility of using bimodal magnetic and light data for indoor localization through experiments. We then design the DeepML system, which first builds bimodal images by data preprocessing, and then trains a deep LSTM network to extract the location features. Newly received magnetic field and light intensity data are then exploited for estimating the location of the mobile device using an improved probabilistic method. Our extensive experiments verify the effectiveness of the proposed DeepML system.