Abstract Since the availability of raw global navigation satellite system (GNSS) measurements in Android devices in 2016, smartphone users have acquired the ability to not only process GNSS position, velocity, and time (PVT) solutions from chipsets, but also to utilize raw measurements to carry out their own GNSS postprocessing. Consequently, rather than relying on black-box algorithms inside the chipsets, it has become possible to understand and configure the navigation algorithms. To date, most publicly available navigation algorithms and corresponding publications that use Android raw GNSS measurements have focused on GNSS-only solutions. Furthermore, the top three participants of the Google Smartphone Decimeter Challenge in 2021 and 2022 were either unsuccessful in implementing an effective extended Kalman filter (EKF) using both GNSS and inertial navigation sensors (INSs) or found no significant improvements to their solutions arising from the use of non-GNSS sensors. This paper provides insight into the challenges of effectively implementing a traditional GNSS/INS EKF for smartphones and explores the reason behind the limited performance enhancements compared with GNSS-only solutions under benign environments. This work addresses these issues and provides solutions to alleviate these shortcomings by describing an algorithm to successfully fuse inertial sensors with raw GNSS measurements. The algorithm provides a robust solution by using not only the code, Doppler, and carrier-phase measurements in the PVT computation, but also the inertial sensors to assist in GNSS fault detection and exclusion (FDE) and to improve solution accuracy and availability. The novelties of this paper lie in the incorporation of a tightly coupled GNSS/INS EKF for sensor fusion, single-differenced GNSS measurements to eliminate the effects of receiver clock components, empirical modeling of GNSS and INS errors for statistical accuracy, utilization of both carrier-phase and Doppler measurements for accurate benign and challenging environment operations, and the application of INS for GNSS measurement FDE and navigation availability improvements.
Lee et al. (Sun,) studied this question.