Improving the stochastic model for code pseudorange observations from Android smartphones

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autoren

  • Berkay Bahadur
  • Steffen Schön

Organisationseinheiten

Externe Organisationen

  • Hacettepe University
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Aufsatznummer148
Seitenumfang17
FachzeitschriftGPS solutions
Jahrgang28
Ausgabenummer3
Frühes Online-Datum25 Juni 2024
PublikationsstatusVeröffentlicht - Juli 2024

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.

ASJC Scopus Sachgebiete

Zitieren

Improving the stochastic model for code pseudorange observations from Android smartphones. / Bahadur, Berkay; Schön, Steffen.
in: GPS solutions, Jahrgang 28, Nr. 3, 148, 07.2024.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Bahadur B, Schön S. Improving the stochastic model for code pseudorange observations from Android smartphones. GPS solutions. 2024 Jul;28(3):148. Epub 2024 Jun 25. doi: 10.1007/s10291-024-01690-y
Bahadur, Berkay ; Schön, Steffen. / Improving the stochastic model for code pseudorange observations from Android smartphones. in: GPS solutions. 2024 ; Jahrgang 28, Nr. 3.
Download
@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",

}

Download

TY - JOUR

T1 - Improving the stochastic model for code pseudorange observations from Android smartphones

AU - Bahadur, Berkay

AU - Schön, Steffen

N1 - Publisher Copyright: © The Author(s) 2024.

PY - 2024/7

Y1 - 2024/7

N2 - 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.

AB - 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.

KW - GNSS

KW - Positioning

KW - Robust Kalman filter

KW - Smartphone

KW - Stochastic model

UR - http://www.scopus.com/inward/record.url?scp=85196760870&partnerID=8YFLogxK

U2 - 10.1007/s10291-024-01690-y

DO - 10.1007/s10291-024-01690-y

M3 - Article

AN - SCOPUS:85196760870

VL - 28

JO - GPS solutions

JF - GPS solutions

SN - 1080-5370

IS - 3

M1 - 148

ER -