MapLoc: LSTM-Based Location Estimation Using Uncertainty Radio Maps

Abstract

With the growing demand for location-based services, fingerprint has become a hot topic in the area of Internet of Things (IoT). However, the performance of fingerprinting-based indoor localization systems is usually affected by the quality and granularity of fingerprints. In this paper, we present MapLoc, a Long Short-term Memory (LSTM)-based indoor localization system that takes advantage of the continuous indoor uncertainty maps created using both earth magnetic field readings and WiFi received signal strengths (RSS). A Deep Gaussian Process (DGP) model is trained to create indoor radio maps with confidence intervals, which are referred as uncertainty maps. Utilizing the uncertainty maps, an LSTM based location prediction model is pre-trained with artificial trajectory data sampled from the uncertainty maps, and then fine-tuned with the signal measurements collected in the field. In the training process, auxiliary outputs are implemented to overcome overfitting and improve the robustness of the system. Our extensive experiments demonstrate the outstanding performance of the proposed MapLoc system.