Smartphone RTK positioning with multi-frequency and multi-constellation raw observations: GPS L1/L5, Galileo E1/E5a, BDS B1I/B1C/B2a

Wang, Yingzhe; Li, Lan; Zhu, Feng; Tao, Xianlu

doi:10.1007/s00190-023-01731-3

Cited by 17 publications

(5 citation statements)

References 37 publications

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“…Adding GLONASS does not always improve the ZTD estimation accuracy. This variability may be attributed to the inferior quality of the GLONASS observations collected by smartphones, as suggested in previous studies (Wang et al, 2023;Tao et al, 2023). Overall, the https://doi.org/10.5194/egusphere-2024-66 Preprint.…”

Section: Evaluation Of Zenith Total Delaysmentioning

confidence: 76%

Determination of High-Precision Tropospheric Delays Using Crowdsourced Smartphone GNSS Data

Pan,

Kłopotek,

Crocetti

et al. 2024

Preprint

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Abstract. The Global Navigation Satellite System (GNSS) is a key asset for tropospheric monitoring. Currently, GNSS meteorology relies primarily on geodetic-grade stations. However, such stations are too costly to be densely deployed, which limits the contribution of GNSS to tropospheric monitoring. In 2016, Google released the raw GNSS measurement application programming interface for smartphones running on Android version 7.0 and higher. Since nowadays there are billions of Android smartphones worldwide, utilizing those devices for atmospheric monitoring represents a remarkable scientific opportunity. In this study, smartphone GNSS data collected in Germany as part of the Application of Machine Learning Technology for GNSS IoT Data Fusion (CAMALIOT) crowdsourcing campaign in 2022 were utilized to investigate this idea. Approximately twenty thousand raw GNSS observation files were collected there during the campaign. First, a dedicated data processing pipeline was established that consists of two major parts: machine learning (ML)-based data selection and ionosphere-free Precise Point Positioning (PPP)-based Zenith Total Delay (ZTD) estimation. The proposed method was validated with a dedicated smartphone data collection experiment conducted on the rooftop of the ETH campus. The results confirmed that ZTD estimates of mm-level precision could be achieved with smartphone data collected in an open-sky environment. The impacts of observation time span and utilization of multi-GNSS observations on ZTD estimation were also investigated. Subsequently, the crowdsourced data from Germany were processed by PPP with the ionospheric delays interpolated using observations from surrounding SAPOS (Satellite Positioning Service of the German State Survey) GNSS stations. The ZTDs derived from ERA5 and an ML-based ZTD product served as benchmarks. The results revealed that an accuracy of better than 10 mm can be achieved by utilizing selected high-quality crowdsourced smartphone data. This study marks the first successful demonstration of high-precision ZTD determination with crowdsourced smartphone GNSS data and reveals success factors and current limitations.

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Section: Evaluation Of Zenith Total Delaysmentioning

confidence: 76%

Determination of High-Precision Tropospheric Delays Using Crowdsourced Smartphone GNSS Data

Pan,

Kłopotek,

Crocetti

et al. 2024

Preprint

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“…be around 5~10 m. Previous studies have conducted researches on the accuracy of smartphone-and GPS-based location method. In terms of location accuracy by smartphone without post-processing, Merry and Bettinger [48] obtained an accuracy range of 7~13 m, Zandbergen [49] reported an accuracy of around 10 m, Menard et al [50] found an accuracy range of 5~10 m, Garnett and Stewart [51] pointed out an accuracy of around 6.5 m. Furthermore, a meter-level accuracy could be achieved by the establishment of a differential correction method [52], an improved Hatch filtering method [53], and the RTK-based methods [54,55]. Given this challenge in crack location, part of the reason for the crack description function shown in Figure 13 was to provide assistance for crack location description.…”

Section: Crack Detection Testmentioning

confidence: 99%

Establishment and Application of Crowd-Sensing-Based System for Bridge Structural Crack Detection

Yuan

Jin

2023

Applied Sciences

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The inspection of bridge structural cracks is essential to the structural safety evaluation and could provide reference for preventive maintenance. The traditional bridge structure inspection methods rely heavily on trained engineers with professional equipment. While such kind of way could provide reliable crack inspection data, the enormous amount of existing bridges waiting for inspection challenges the efficiency of these methods. Fortunately, the development of smartphones facilitates the possibility of making the pedestrian taking smartphones a mobile sensing node, which is able to collect crack information such as images and locations. At the same time, the booming deep learning methods could offer remarkable crack detection capacity to deal with the crack images automatically. Given this consideration, this paper established a crowd-sensing-based system for bridge structural crack detection. The system was composed of the cloud-based management platform and the mobile based application (APP) for crack information collection. The mobile-based APP was used by the volunteer pedestrians to collect the crack images as well as the locations, and the location accuracy was estimated to be around 5~10 m. Meanwhile, the cloud-based management platform was used for the management of the users and the collected crack information uploaded by all of the volunteers. A deep neural network was used to deal with the crack detection tasks and evaluate the quality of the collected images to see if they could be fitted for crack detection in bridge inspection works.

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“…In a real-time kinematics (RTK) mode, decimeter or even centimeter positioning accuracy can be realized in static open spaces under ideal conditions [ 6 , 7 , 8 , 9 ]. However, dynamic positioning in urban environments presents challenges due to observational noise and multipath influences, making ambiguity difficult to fix and reducing the average positioning accuracy to meter levels, thereby yielding suboptimal results [ 10 , 11 , 12 ]. Consequently, a discernible gap persists between smartphone-based location services and the high-precision positioning application demands of public life.…”

Section: Introductionmentioning

confidence: 99%

A Stochastic Model Based on Optimal Satellite Subset Selection Strategy for Smartphone Pseudorange Relative Positioning

Deng,

Wang,

Wei

et al. 2024

Sensors

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In order to overcome the limitations of traditional stochastic models for smartphones, we introduce a double-difference code pseudorange residual (DDPR)-dependent stochastic model based on an optimal satellite subset, with the goal of mitigating the constraints imposed by the quality of GNSS observations in smartphones on the accuracy and reliability of phone-based GNSS positioning. In our methodology, the satellite selection process involved considering the integrated carrier-to-noise density ratio (C/N0) index of both the reference station and the smartphone, enabling us to construct a satellite subset characterized by superior observation quality. Furthermore, by leveraging the optimal subset of satellites and incorporating the C/N0-dependent stochastic model, we could determine the approximate location of the terminal through pseudorange differential positioning. Subsequently, we estimated the DDPRs for all satellites and utilized these values as prior information to build a stochastic model of the observations. Our findings indicate that in occluded environments, the DDPR-dependent stochastic model significantly enhances positioning accuracy for both the Huawei Mate40 and P40 terminals compared to the C/N0-dependent model. Numerically, the improvements in the north (N), east (E), and up (U) directions were approximately 30%, 32%, and 34% for the Mate40, and 26%, 33%, and 24% for the P40 terminal. This suggests that the proposed DDPR-dependent stochastic model effectively identifies and mitigates large gross error signals caused by multipath and non-line-of-sight (NLOS) signals, thereby assigning lower weights to these problematic observations and ultimately enhancing positioning accuracy. Moreover, the weighting method involves minimal computations and is straightforward to implement, making it particularly suitable for GNSS positioning applications on smartphones in complex urban environments.

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Smartphone RTK positioning with multi-frequency and multi-constellation raw observations: GPS L1/L5, Galileo E1/E5a, BDS B1I/B1C/B2a

Cited by 17 publications

References 37 publications

Determination of High-Precision Tropospheric Delays Using Crowdsourced Smartphone GNSS Data

Determination of High-Precision Tropospheric Delays Using Crowdsourced Smartphone GNSS Data

Establishment and Application of Crowd-Sensing-Based System for Bridge Structural Crack Detection

A Stochastic Model Based on Optimal Satellite Subset Selection Strategy for Smartphone Pseudorange Relative Positioning

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