ARPDR++: Exploiting local-global temporal modeling for smartphone-based indoor pedestrian localization

The increasing prevalence of mobile computing has made Pedestrian Dead Reckoning (PDR) one of the most promising and attractive indoor localization techniques for ubiquitous applications. However, existing PDR approaches are either sensitive to various users or suffer from accumulated errors that cause position drifts. To address these issues, this paper proposes ARPDR++, an accurate and robust PDR approach that improves the accuracy and robustness of indoor localization methods. ARPDR++ introduces a novel step counting algorithm based on motion models that deeply exploits inertial sensor data. We combine step counting with adaptive thresholding to personalize the PDR system for different users. Furthermore, we propose a novel stride-heading model with a deep neural network to predict stride lengths and walking orientations, which significantly reduces displacement errors. Experimental results on public datasets demonstrate that ARPDR++ outperforms the state-of-the-art PDR methods.