Analysis of Different Weighting Functions of Observations for GPS and Galileo Precise Point Positioning Performance

Kiliszek, Damian; Kroszczyński, Krzysztof; Araszkiewicz, Andrzej

doi:10.3390/rs14092223

Cited by 5 publications

(5 citation statements)

References 71 publications

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“…The zenith hydrostatic delay of the troposphere is corrected using the Saastamoinen model [33], while the zenith wet delay is estimated as the parameter. The carrier phase ambiguities are estimated as float solutions [34]. The station coordinates of the static PPP are estimated as a constant, while it is estimated as white noise in the kinematic model.…”

Section: Ppp Accuracy Evaluationmentioning

confidence: 99%

Performance of Multi-GNSS in the Asia-Pacific Region: Signal Quality, Broadcast Ephemeris and Precise Point Positioning (PPP)

Huang

Wang

et al. 2022

Remote Sensing

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Since BeiDou Navigation Satellite System (BDS) and Japan’s Quasi-Zenith Satellite System (QZSS) have more visible satellites in the Asia-Pacific region, and navigation satellites of Global Positioning System (GPS), Galileo satellite navigation system (Galileo), and GLONASS satellite navigation system (GLONASS) are uniformly distributed globally, the service level of multi-mode Global Navigation Satellite System (GNSS) in the Asia-Pacific region should represent the best service capability. Based on the observation data of 10 Multi-GNSS Experiment (MGEX) stations, broadcast ephemeris and precision ephemeris from 13 to 19 October 2021, this paper comprehensively evaluated the service capability of multi-GNSS in the Asia-Pacific region from three aspects of observation data quality, broadcast ephemeris performance, and precision positioning level. The results show that: (1) the carrier-to-noise-density ratio (C/N0) quality of the GPS and Galileo is the best, followed by BDS and GLONASS, and QZSS is the worst. GPS, BDS-2, GLONASS, and QZSS pseudorange multipath values range from 0 to 0.6 m, while Galileo system and BDS-3 pseudorange multipath values range from 0 to 0.8 m. (2) In terms of broadcast ephemeris accuracy, BDS-3 broadcast ephemeris has the best orbit, and the three-dimensional (3D) Root Mean Square (RMS) is 0.21 m; BDS-2 was the worst, with a 3D RMS of 1.99 m. The broadcast ephemeris orbits of GPS, Galileo, QZSS, and GLONASS have 3D RMS of 0.60 m, 0.62 m, 0.83 m, and 1.27 m, respectively. For broadcast ephemeris clock offset: Galileo has the best performance, 0.61 ns, GLONASS is the worst, standard deviation (STD) is 3.10 ns, GPS, QZSS, BDS-3 and BDS-2 are 0.65 ns, 0.75 ns, and 1.72 ns, respectively. For signal-in-space ranging errors (SISRE), the SISRE results of GPS and Galileo systems are the best, fluctuating in the range of 0 m–2 m, followed by QZSS, BDS-3, Galileo, and BDS-2. (3) GPS, BDS, GLONASS, Galileo, GPS/QZSS, and BDS/QZSS were used for positioning experiments. In static PPP, the convergence time and positioning accuracy of GPS show the best performance. The positioning accuracy of GPS/QZSS and BDS/QZSS is improved compared with that of GPS and BDS. In terms of kinematic PPP, the convergence time and positioning accuracy of GPS/QZSS and BDS/QZSS are improved compared with that of GPS and BDS. In addition to GLONASS and Galileo systems, the other combinations outperformed 3 cm, 3 cm, and 5 cm in the east, north, and up directions.

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Section: Ppp Accuracy Evaluationmentioning

confidence: 99%

Performance of Multi-GNSS in the Asia-Pacific Region: Signal Quality, Broadcast Ephemeris and Precise Point Positioning (PPP)

Huang

Wang

et al. 2022

Remote Sensing

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“…Precise geodetic applications rely on carrier phase observables, hence different weights must be assigned to GNSS observations, such as higher weights assigned for high precision observations, resulting in a meaningful impact of the estimated parameters. A low elevation satellite can negatively impact the final estimates, due to the higher errors caused by the atmosphere and multipath, generating higher noise and lower signal strength [13]. Thus, the weighting procedure of the observations is related to the elevation angle of the satellites due to a possible strong correlation between the elevation angle and the observation noise [13,14].…”

Section: Introductionmentioning

confidence: 99%

“…A low elevation satellite can negatively impact the final estimates, due to the higher errors caused by the atmosphere and multipath, generating higher noise and lower signal strength [13]. Thus, the weighting procedure of the observations is related to the elevation angle of the satellites due to a possible strong correlation between the elevation angle and the observation noise [13,14].…”

Section: Introductionmentioning

confidence: 99%

“…Chengfa et al (2011) conducted research on the stochastic modelling of GNSS observation for precise point positioning (PPP) adopting an elevation angle and carrier-to-noisedensity ratio (C/N0), and as a consequence they obtained an improvement on position accuracy [19]. Moreover, [20]'s research presented higher position accuracy when using different weights for each GNSS system, whereas [13] used eight different weighting procedures dependent on the elevation angle and the results showed a visible impact on the estimation of the vertical component.…”

Section: Introductionmentioning

confidence: 99%

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Comprehensive Analysis on GPS Carrier Phase under Various Cutoff Elevation Angles and Its Impact on Station Coordinates’ Repeatability

Nistor,

Suba,

Buda

et al. 2024

Remote Sensing

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When processing the carrier phase, the global navigation satellite system (GNSS) grants the highest precision for geodetic measurements. The analysis centers (ACs) from the International GNSS Service (IGS) provide different data such as precise clock data, precise orbits, reference frame, ionosphere and troposphere data, as well as other geodetic products. Each individual AC has its own strategy for delivering the abovementioned products, with one of the key elements being the cutoff elevation angle. Typically, this angle is arbitrarily chosen using generic values without studying the impact of this choice on the obtained results, in particular when very precise positions are considered. This article addresses this issue. To this end, the article has two key sections, and the first is to evaluate the impact of using the two different cutoff elevation angles that are most widely used: (a) 3 degrees cutoff and (b) 10 degrees cutoff elevation angle. This analysis is completed in two major parts: (i) the analysis of the root mean square (RMS) for the carrier phase and (ii) the analysis of the station position in terms of repeatability. The second key section of the paper is a comprehensive carrier phase analysis conducted by adopting a new approach using a mean of the 25-point average RMS (A-RMS) and the single-point RMS and using an ionosphere-free linear combination. By using the ratio between the 25-point average RMS and the single-point RMS we can define the type of scatter that dominates the phase solution. The analyzed data span a one-year period. The tested GNSS stations belong to the EUREF Permanent Network (EPN) and the International GNSS Service (IGS). These comprise 55 GNSS stations, of which only 23 GNSS stations had more than 95% data availability for the entire year. The RMS and A-RMS are analyzed in conjunction with the precipitable water vapor (PWV), which shows clear signs of temporal correlation. Of the 23 GNSS stations, three stations show an increase of around 50% of the phase RMS when using a 3° cutoff elevation angle, and only four stations have a difference of 5% between the phase RMS when using both cutoff elevation angles. When using the A-RMS, there is an average improvement of 37% of the phase scatter for the 10° cutoff elevation angle, whereas for the 3° cutoff elevation angle, the improvement is around 33%. Based on studying this ratio, four stations indicate that the scatter is dominated by the stronger-than-usual dominance of long-period variations, whereas the others show short-term noise. In terms of station position repeatability, the weighted root mean square (WRMS) is used as an indicator, and the results between the differences of using a 3° and 10° cutoff elevation angle strategy show a difference of −0.16 mm for the North component, −0.21 mm for the East component and a value of −0.75 mm for the Up component, indicating the importance of using optimal cutoff angles.

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“…With the advancement of GNSS technology, this step has become increasingly challenging with the capability of the receivers to receive tens of satellite signals at each time epoch. The observation noise is related to single-link features such as the signal-to-noise-power-density ratio (C/N 0 ) [4] and/or the elevation angle (θ) [5]. Accordingly, widely used pre-selection methods rely on these features for the exclusion or the mitigation of satellites' measurements that may cause large localization errors [6,7].…”

Section: Introductionmentioning

confidence: 99%

LSTM-Based GNSS Localization Using Satellite Measurement Features Jointly with Pseudorange Residuals

Sbeity,

Villien,

Denis

et al. 2024

Sensors

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In the Global Navigation Satellite System (GNSS) context, the growing number of available satellites has led to many challenges when it comes to choosing the most-accurate pseudorange contributions, given the strong impact of biased measurements on positioning accuracy, particularly in single-epoch scenarios. This work leverages the potential of machine learning in predicting linkwise measurement quality factors and, hence, optimize measurement weighting. For this purpose, we used a customized matrix composed of heterogeneous features such as conditional pseudorange residuals and per-link satellite metrics (e.g., carrier-to-noise-power-density ratio and its empirical statistics, satellite elevation, carrier phase lock time). This matrix is then fed as an input to a long short-term memory (LSTM) deep neural network capable of exploiting the hidden correlations between these features relevant to positioning, leading to the predictions of efficient measurement weights. Our extensive experimental results on real data, obtained from extensive field measurements, demonstrate the high potential of our proposed solution, which is able to outperform traditional measurement weighting and selection strategies from the state-of-the-art. In addition, we included detailed illustrations based on representative sessions to provide a concrete understanding of the significant gains of our approach, particularly in strongly GNSS-challenged operating conditions.

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Analysis of Different Weighting Functions of Observations for GPS and Galileo Precise Point Positioning Performance

Cited by 5 publications

References 71 publications

Performance of Multi-GNSS in the Asia-Pacific Region: Signal Quality, Broadcast Ephemeris and Precise Point Positioning (PPP)

Performance of Multi-GNSS in the Asia-Pacific Region: Signal Quality, Broadcast Ephemeris and Precise Point Positioning (PPP)

Comprehensive Analysis on GPS Carrier Phase under Various Cutoff Elevation Angles and Its Impact on Station Coordinates’ Repeatability

LSTM-Based GNSS Localization Using Satellite Measurement Features Jointly with Pseudorange Residuals

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