2023/09/16 by Xu Weng, Weng, Xu, Keck Voon Ling +1 · 2 citations
Engineering · #Android (operating system) #Artificial intelligence #Computer science #Extended Kalman filter #FOS: Electrical engineering #GNSS applications #GNSS augmentation #GNSS positioning and interference #Geodesy #Geodetic datum #Geography #Global Positioning System #Indoor and Outdoor Localization Technologies #Inertial Sensor and Navigation #Kalman filter #Mobile device #Real-time computing #Signal Processing (eess.SP) #Telecommunications #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2309.08936
openalex publication_date 2023/09/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Android raw Global Navigation Satellite System (GNSS) measurements are expected to bring smartphones power to take on demanding localization tasks that are traditionally performed by specialized GNSS receivers. The hardware constraints, however, make Android raw GNSS measurements much noisier than geodetic-quality ones. This study elucidates the principles of localization using Android raw GNSS measurements and leverages Moving Horizon Estimation (MHE), Extended Kalman Filter (EKF), and Rauch-Tung-Striebel (RTS) smoother for noise suppression. Experimental results show that the RTS smoother achieves the best positioning performance, with horizontal positioning errors significantly reduced by 76.4% and 46.5% in static and dynamic scenarios compared with the baseline weighted least squares (WLS) method. Our codes are available at https://github.com/ailocar/androidGnss.