Towards Sub-meter Positioning using Android Raw GNSS Measurements

Psychas, Dimitrios; Bruno, Jon; Massarweh, Lotfi; Darugna, Francesco

doi:10.33012/2019.17077

Cited by 32 publications

(15 citation statements)

References 3 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: 85%

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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: 85%

“…Calculations were performed in single point positioning (SPP) mode. In case of the longer sessions, the accuracy might be improved by a larger number of observations; studies by other authors show much more accurate results, however, which included longer measurement sessions, e.g., [96,97].…”

Section: Adopted Methodologymentioning

confidence: 96%

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

et al. 2021

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“…Wu et al [9] evaluated the PPP solution in post-processing, using a dual frequency smartphone in a static position, and could perform a horizontal accuracy of 0.50 m in a 107 min convergence period. Psychas et al [14] required only 34 min to achieve a positioning error below 1 m.…”

Section: Introductionmentioning

confidence: 99%

An Approach to Improving GNSS Positioning Accuracy Using Several GNSS Devices

2021

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Single point positioning (SPP) mode, related to pseudorange measurements, limits the level of accuracy to several meters in open sky and to several dozens of meters in urban canyons. This paper explores the effect of using a large number of SPP observations from low-cost global navigation system (GNSS) receivers, smartphones, and handheld GNSS units. Data segmentation and bootstrapping statistical methods were used to obtain the deviation, which can describe the accuracy of the large sample. The empirical test recording data showed that the error may achieve a sub-meter horizontal accuracy by the simple process of increasing the measurements of smartphones and handheld GNSS units. However, the drawback is the long period of time required. To reduce the satellite tracking time, a least squares solution network was applied over all the recorded data, assisted by the external geometric conditions. The final goal was to obtain the absolute positioning and associated deviations of one vertex from three or five GNSS receivers positioned on a network. The process was tested in three geodetic network examples. The results indicated that the enhanced SPP mode was able to improve its accuracy. Errors of several meters were reduced to values close to 50 cm in 25–37 min periods.

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“…The global navigation satellite system (GNSS) positioning technique has been implemented in various applications, e.g., geodetic surveying and mapping [1,2], vehicle and pedestrian navigation [3,4], earthquake and deformation monitoring [5] and meteorology [6,7]. Therefore, many GNSS positioning based studies have been emerging in the last two decades.…”

Section: Introductionmentioning

confidence: 99%

Kriging Interpolation in Modelling Tropospheric Wet Delay

Zhao

Verhagen

et al. 2020

Atmosphere

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This contribution implements the Kriging interpolation in predicting the tropospheric wet delays using global navigation satellite system networks. The predicted tropospheric delays can be used in strengthening the precise point positioning models and numerical weather prediction models. In order to evaluate the performances of the Kriging interpolation, a sparse network with 8 stations and a dense network with 19 stations from continuously operating reference stations (CORS) of the Netherlands are selected as the reference. In addition, other 15 CORS stations are selected as users, which are divided into three blocks: 5 stations located approximately in the center of the networks, 5 stations on the edge of the networks and 5 stations outside the networks. The zenith tropospheric wet delays are estimated at the network and user stations through the ionosphere-free positioning model; meanwhile, the predicted wet delays at the user stations are generated by the Kriging interpolation in the use of the tropospheric estimations at the network. The root mean square errors (RMSE) are calculated by comparing the predicted wet delays and estimated wet delays at the same user station. The results show that RMSEs of the stations inside the network are at a sub-centimeter level with an average value of 0.74 cm in the sparse network and 0.69 cm in the dense network. The stations on edge and outside the network can also achieve 1-cm level accuracy, which overcomes the limitation that accurate interpolations can only be attained inside the network. This contribution also presents an insignificant improvement of the prediction accuracy from the sparse network to the dense network over 1-year’s data processing and a seasonal effect on the tropospheric wet delay predictions.

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Towards Sub-meter Positioning using Android Raw GNSS Measurements

Cited by 32 publications

References 3 publications

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

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

An Approach to Improving GNSS Positioning Accuracy Using Several GNSS Devices

Kriging Interpolation in Modelling Tropospheric Wet Delay

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