An analysis of multi-GNSS observations tracked by recent Android smartphones and smartphone-only relative positioning results

Paziewski, Jacek; Fortunato, Marco; Mazzoni, Augusto; Odolinski, Robert

doi:10.1016/j.measurement.2021.109162

Cited by 70 publications

(36 citation statements)

References 58 publications

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“…This confirmed the results obtained by other authors [13,14]. However, the smartphone data cannot provide equivalent performance to that provided by geodetic-grade receivers (with one of the main reasons being the difference in the quality of the antenna used in smartphones) [18,96,97]. The rapid development of algorithms and measuring techniques adapted to the specifics of mobile devices can be expected in the near future.…”

Section: A Comparison Of the Methods For Altitude Determination: Prossupporting

confidence: 86%

“…This facilitates the use of measurement and calculation methods previously reserved for precise geodetic techniques. Since the release of Android API 24, numerous works have been published containing descriptions of measurement tests and calculation experiments that are intended to determine the degree of usability of the available GNSS measurement data using RTK and/or PPP modes [13][14][15][16][17][18]. These papers contain analyses of observations recorded during carefully prepared experiments at appropriately selected measurement points.…”

Section: Introductionmentioning

confidence: 99%

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Determining Peak Altitude on Maps, Books and Cartographic Materials: Multidisciplinary Implications

et al. 2021

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Mountain peaks and their altitude have been of interest to researchers across disciplines. Measurement methods and techniques have changed and developed over the years, leading to more accurate measurements and, consequently, more accurate determination of peak altitudes. This research transpired due to the frequency of misstatements found in existing sources including books, maps, guidebooks and the Internet. Such inaccuracies have the potential to create controversy, especially among peak-baggers in pursuit of climbing the highest summits. The Polish Sudetes Mountains were selected for this study; 24 summits in the 14 mesoregions were measured. Measurements were obtained employing the global navigation satellite system (GNSS) and light detection and ranging (LiDAR), both modern and highly precise techniques. Moreover, to determine the accuracy of measurements, several of the summits were measured using a mobile phone as an additional method. We compare GNSS vs. LiDAR and verify the level of confidence of peak heights obtained by automatic methods from LiDAR data alone. The GNSS receiver results showed a discrepancy of approximately 10 m compared with other information sources examined. Findings indicate that the heights of peaks presented in cartographic materials are inaccurate, especially in lesser-known mountain ranges. Furthermore, among all the mountain ranges examined, the results demonstrated that five of the summits were no longer classed as the highest, potentially impacting tourist perceptions and subsequent visitation. Overall, due to the topographical relief characteristics and varying vegetation cover of mountains, we argue that the re-measuring procedure should comprise two steps: (1) develop high-resolution digital elevation models (DEMs) based on LiDAR; (2) assumed heights should be measured using precise GNSS receivers. Unfortunately, due to the time constraints and the prohibitive costs of GNSS, LiDAR continues to be the most common source of new altitude data.

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Section: A Comparison Of the Methods For Altitude Determination: Prossupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Determining Peak Altitude on Maps, Books and Cartographic Materials: Multidisciplinary Implications

et al. 2021

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“…Previous studies have found that the data quality of GPS and Galileo L5/E5 frequency is better than L1/E1 [ 22 , 23 , 24 ], and the use of L1/E1 and L5/E5 dual-frequency data can effectively weaken ionospheric delay. However, due to the fact that the number of dual-frequency data obtained by the smartphone is small, the utilization rate of the dual-frequency data is low.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Smartphone Positioning and Accuracy Analysis Based on Real-Time Regional Ionospheric Correction Model

Liu

Gao

Peng

et al. 2021

Sensors

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As one of the main errors that affects Global Navigation Satellite System (GNSS) positioning accuracy, ionospheric delay also affects the improvement of smartphone positioning accuracy. The current ionospheric error correction model used in smartphones has a certain time delay and low accuracy, which is difficult to meet the needs of real-time positioning of smartphones. This article proposes a method to use the real-time regional ionospheric model retrieved from the regional Continuously Operating Reference Stations (CORS) observation data to correct the GNSS positioning error of the smartphone. To verify the accuracy of the model, using the posterior grid as the standard, the electron content error of the regional ionospheric model is less than 5 Total Electron Content Unit (TECU), which is about 50% higher than the Klobuchar model, and to further evaluate the impact of the regional ionosphere model on the real-time positioning accuracy of smartphones, carrier-smoothing pseudorange and single-frequency Precise Point Positioning (PPP) tests were carried out. The results show that the real-time regional ionospheric model can significantly improve the positioning accuracy of smartphones, especially in the elevation direction. Compared with the Klobuchar model, the improvement effect is more than 34%, and the real-time regional ionospheric model also shortens the convergence time of the elevation direction to 1 min. (The convergence condition is that the range of continuous 20 s is less than 0.5 m).

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“…The RMS error of the horizontal component was 0.3-0.6 m in static mode, 0.4-0.7 m in walking condition and 0.85 m in vehicular experiment. Paziewski et al (2019) provided a comprehensive evaluation on the quality of the smartphone observation with a specific focus on the anomalies presented in the carrierphase and code observations from a GNSS smartphone due to the duty cycling. They showed that the smartphone GNSS observations are affected not only by the high measurement noise and multipath but also by the anomalies such as duty cycling and gradual accumulation of phase errors.…”

Section: Relative Positioningmentioning

confidence: 99%

“…Calibrating IPB allows the recovery of the integer nature of carrier-phase ambiguities resulting in about 30%-80% improvement in positioning accuracy compared with the ambiguity-float solutions. Paziewski et al (2021) also investigated the feasibility of integer ambiguity resolution by computing the DD phase residuals of smartphones. Based on the results, the DD phase residuals suffers from unwanted effects (long-term drift) and noise caused not preserving the integer nature of ambiguities.…”

Section: Relative Positioningmentioning

confidence: 99%

GNSS smartphones positioning: advances, challenges, opportunities, and future perspectives

Zangeneh-Nejad

Gao

2021

Satell Navig

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Starting from 2016, the raw Global Navigation Satellite System (GNSS) measurements can be extracted from the Android Nougat (or later) operating systems. Since then, GNSS smartphone positioning has been given much attention. A high number of related publications indicates the importance of the research in this field, as it has been doing in recent years. Due to the cost-effectiveness of the GNSS smartphones, they can be employed in a wide variety of applications such as cadastral surveys, mapping surveying applications, vehicle and pedestrian navigation and etc. However, there are still some challenges regarding the noisy smartphone GNSS observations, the environment effect and smartphone holding modes and the algorithm development part which restrict the users to achieve high-precision smartphone positioning. In this review paper, we overview the research works carried out in this field with a focus on the following aspects: first, to provide a review of fundamental work on raw smartphone observations and quality assessment of GNSS observations from major smart devices including Google Pixel 4, Google Pixel 5, Xiaomi Mi 8 and Samsung Ultra S20 in terms of their signal strengths and carrier-phase continuities, second, to describe the current state of smartphone positioning research field until most recently in 2021 and, last, to summarize major challenges and opportunities in this filed. Finally, the paper is concluded with some remarks as well as future research perspectives.

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An analysis of multi-GNSS observations tracked by recent Android smartphones and smartphone-only relative positioning results

Cited by 70 publications

References 58 publications

Determining Peak Altitude on Maps, Books and Cartographic Materials: Multidisciplinary Implications

Determining Peak Altitude on Maps, Books and Cartographic Materials: Multidisciplinary Implications

Smartphone Positioning and Accuracy Analysis Based on Real-Time Regional Ionospheric Correction Model

GNSS smartphones positioning: advances, challenges, opportunities, and future perspectives

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