Improving the stochastic model for code pseudorange observations from Android smartphones

authored by: Berkay Bahadur, Steffen Schön
Abstract: In recent years, there has been increasing attention to positioning, navigation, and timing applications with smartphones. Because of frequently disrupted carrier phase observations, code observations remain critical for smartphone-based positioning. Considering a realistic stochastic model is mandatory to obtain the utmost positioning performance, this study proposes a sound stochastic approach for code observations from Android smartphones. The proposed approach includes a modified version of the SIGMA-ɛ variance model with different coefficients for each GNSS constellation and a robust Kalman filter method. First the noise characteristics of observations from the Xiaomi Mi 8 smartphone are analyzed utilizing code-minus-phase combinations to estimate the coefficients for each GNSS constellation. This includes the determination of a variance model as well as a check of the probability distribution. Finally, the proposed approach is validated in the positioning domain using single-frequency code observation-based real-time standalone positioning. The results show that more than 95% of observations follow the normal distribution when the proposed approach is applied. Compared with the conventional stochastic approach, including a C/N0-dependent model and standard Kalman filter, it improves the positioning accuracy by 45.8% in a static experiment, while its improvement is equal to 26.6% in a kinematic experiment. For the static and kinematic experiments, in 50% of the epochs, the 3D positioning errors are smaller than 3.0 m and 3.4 m for the proposed stochastic approach. The results exhibit that the stochastic properties of code observations from smartphones can be successfully represented by the proposed approach.
Organisation(s): Institute of Geodesy
External Organisation(s): Hacettepe University
Type: Article
Journal: GPS solutions
Volume: 28
No. of pages: 17
ISSN: 1080-5370
Publication date: 07.2024
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Earth and Planetary Sciences(all)
Electronic version(s): https://doi.org/10.1007/s10291-024-01690-y (Access: Open)

BibTeX

@article{5ddfc343cd36401e8f93fc35102e0401,
title = "Improving the stochastic model for code pseudorange observations from Android smartphones",
abstract = "In recent years, there has been increasing attention to positioning, navigation, and timing applications with smartphones. Because of frequently disrupted carrier phase observations, code observations remain critical for smartphone-based positioning. Considering a realistic stochastic model is mandatory to obtain the utmost positioning performance, this study proposes a sound stochastic approach for code observations from Android smartphones. The proposed approach includes a modified version of the SIGMA-ɛ variance model with different coefficients for each GNSS constellation and a robust Kalman filter method. First the noise characteristics of observations from the Xiaomi Mi 8 smartphone are analyzed utilizing code-minus-phase combinations to estimate the coefficients for each GNSS constellation. This includes the determination of a variance model as well as a check of the probability distribution. Finally, the proposed approach is validated in the positioning domain using single-frequency code observation-based real-time standalone positioning. The results show that more than 95% of observations follow the normal distribution when the proposed approach is applied. Compared with the conventional stochastic approach, including a C/N0-dependent model and standard Kalman filter, it improves the positioning accuracy by 45.8% in a static experiment, while its improvement is equal to 26.6% in a kinematic experiment. For the static and kinematic experiments, in 50% of the epochs, the 3D positioning errors are smaller than 3.0 m and 3.4 m for the proposed stochastic approach. The results exhibit that the stochastic properties of code observations from smartphones can be successfully represented by the proposed approach.",
keywords = "GNSS, Positioning, Robust Kalman filter, Smartphone, Stochastic model",
author = "Berkay Bahadur and Steffen Sch{\"o}n",
note = "Publisher Copyright: {\textcopyright} The Author(s) 2024.",
year = "2024",
month = jul,
doi = "10.1007/s10291-024-01690-y",
language = "English",
volume = "28",
journal = "GPS solutions",
issn = "1080-5370",
publisher = "Springer Nature",
number = "3",
}