Combined GPS and BDS for single-frequency continuous RTK positioning through real-time estimation of differential inter-system biases

Gao, Wang; Meng, Xiaolin; Gao, Chengfa; Pan, Shuguo; Wang, Denghui

doi:10.1007/s10291-017-0687-5

Cited by 49 publications

(42 citation statements)

References 23 publications

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“…Real Time Kinematic positioning has been the issue of several recent research papers aiming to improve its use, such as Khodabandeh and Teunissen (2016), Tatarnikov, Stepanenko and Astakhov (2016) and Gao et al (2018). Besides its benefits, RTK use can encounter limitations related to the baseline, which should not cross 20 km due to the spatial error decorrelation, for instance, those related to atmospheric effects and satellite orbits.…”

Section: Positioning Methodsmentioning

confidence: 99%

Generation and Performance Analysis of GPS and Glonass Virtual Data for Positioning Under Different Ionospheric Conditions

Jerez

Alves

2019

Bol. Ciênc. Geod.

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Several approaches concerning the use in positioning of GNSS (Global Navigation Satellite Systems) can be considered: systems, applied methods and errors that can affect the signals. Following the GLONASS (GLObal NAvigation Satellite System) constellation reestablishment (2011), there was renewed interest in its use with GPS (Global Positioning System). Different possibilities are available concerning applied methods, such as the virtual reference station (VRS) concept (it is possible to obtain data for a virtual station that does not physically exist, using data from a network). One of the main sources of error related to the GNSS signal, is the ionosphere. Many studies have been developed aiming to evaluate GPS positioning quality and influences that can affect it, but there are still several investigation possibilities concerning GLONASS. In this context, this research is intended to assess the GPS/GLONASS virtual data positioning performance considering regions and periods with different ionospheric behavior. A high correlation between the results from virtual and real data (Pearson's correlation coefficients around 0.8) was noticed. GPS/GLONASS data performance presented better mean squared error results compared to GPS alone (average 3D improvement was 45 cm-49%). In addition, it was possible to verify ionosphere influence in the positioning error, taking into account station region and period of the year.

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Section: Positioning Methodsmentioning

confidence: 99%

Generation and Performance Analysis of GPS and Glonass Virtual Data for Positioning Under Different Ionospheric Conditions

Jerez

Alves

2019

Bol. Ciênc. Geod.

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“…The method mentioned above is used to calculate the ISB between GNSSs with overlapping frequencies. In Gao et al [100], for different frequencies of different GNSS systems, to eliminate the differences between systems an inter-system difference model of different frequencies was proposed. Once the differential ISBs in the model are estimated, they can be used to enhance the system's availability in harsh environments.…”

Section: Multiple Constellationsmentioning

confidence: 99%

“…Compared with the pseudolite, multi-constellation can improve the geometry between the receiver and satellites without additional investment.There are currently two multi-constellation GNSS relative positioning combination models: one is loosely combined model and the other is the tightly combined model[100]. The loose combination means that different GNSSs choose their own satellite as the pivot satellite and the double-difference observation equation cannot be formed across systems, while the tight combination means that different GNSSs choose a common pivot satellite and the double-difference observation equation can be formed across systems[100,101]. For tight combinations, even if only one satellite is available in a constellation, it will be fully utilized as long as the total number of available satellites meets the requirements.…”

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confidence: 99%

A Review of Global Navigation Satellite System (GNSS)-Based Dynamic Monitoring Technologies for Structural Health Monitoring

et al. 2019

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In the past few decades, global navigation satellite system (GNSS) technology has been widely used in structural health monitoring (SHM), and the monitoring mode has evolved from long-term deformation monitoring to dynamic monitoring. This paper gives an overview of GNSS-based dynamic monitoring technologies for SHM. The review is classified into three parts, which include GNSS-based dynamic monitoring technologies for SHM, the improvement of GNSS-based dynamic monitoring technologies for SHM, as well as denoising and detrending algorithms. The significance and progress of Real-Time Kinematic (RTK), Precise Point Position (PPP), and direct displacement measurement techniques, as well as single-frequency technology for dynamic monitoring, are summarized, and the comparison of these technologies is given. The improvement of GNSS-based dynamic monitoring technologies for SHM is given from the perspective of multi-GNSS, a high-rate GNSS receiver, and the integration between the GNSS and accelerometer. In addition, the denoising and detrending algorithms for GNSS-based observations for SHM and corresponding applications are summarized. Challenges of low-cost and widely covered GNSS-based technologies for SHM are discussed, and problems are posed for future research.

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“…Currently, most studies only focus on tightly combined relative positioning using observations from the overlapping frequencies between GPS, Galileo, QZSS, and IRNSS (i.e., L1-E1 and L5-E5a). Regarding BDS, existing contributions either focus on the overlapping frequency of BDS-2 B2I and Galileo E5b signals [19,24] or the non-overlapping frequencies between BDS-2 and GPS (i.e., B1I-L1 and B2I-L2) [26][27][28][29][30]. None have focused on the model and performance evaluation of tightly combined precise relative positioning of BDS-3 (especially operational satellites) with other GNSS systems such as GPS, Galileo, etc.…”

Section: Band Frequency (Mhz)mentioning

confidence: 99%

Differential Inter-System Biases Estimation and Initial Assessment of Instantaneous Tightly Combined RTK with BDS-3, GPS, and Galileo

Liu

Wang

et al. 2019

Remote Sensing

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In this contribution, we assess, for the first time, the tightly combined real-time kinematic (RTK) with GPS, Galileo, and BDS-3 operational satellites using observations from their overlapping L1-E1-B1C/L5-E5a-B2a frequencies. First, the characteristics of B1C/B2a signals from BDS-3 operational satellites is evaluated compared to GPS/Galileo L1-E1/L5-E5a signals in terms of observed carrier-to-noise density ratio, pseudorange multipath and noise, as well as double-differenced carrier phase and code residuals using data collected with scientific geodetic iGMAS and commercial M300Pro receivers. It’s demonstrated that the observational quality of B1C/B2a signals from BDS-3 operational satellites is comparable to that of GPS/Galileo L1-E1/L5-E5a signals. Then, we investigate the size and stability of phase and code differential inter-system bias (ISB) between BDS-3/GPS/Galileo B1C-L1-E1/B2a-L5-E5a signals using short baseline data collected with both identical and different receiver types. It is verified that the BDS-3/GPS/Galileo ISBs are indeed close to zero when identical type of receivers are used at both ends of a baseline. Moreover, they are generally present and stable in the time domain for baselines with different receiver types, which can be easily calibrated and corrected in advance. Finally, we present initial assessment of single-epoch tightly combined BDS-3/GPS/Galileo RTK with single-frequency and dual-frequency observations using a formal and empirical analysis, consisting of ambiguity dilution of precision (ADOP), ratio values, the empirical ambiguity resolution success rate, and the positioning accuracy. Experimental results demonstrate that the tightly combined model can deliver much lower ADOP and higher ratio values with respect to the classical loosely combined model whether for GPS/BDS-3 or GPS/Galileo/BDS-3 solutions. The positioning accuracy and the empirical ambiguity resolution success rate are remarkably improved as well, which could reach up to approximately 10%∼60% under poor observational conditions.

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Combined GPS and BDS for single-frequency continuous RTK positioning through real-time estimation of differential inter-system biases

Cited by 49 publications

References 23 publications

Generation and Performance Analysis of GPS and Glonass Virtual Data for Positioning Under Different Ionospheric Conditions

Generation and Performance Analysis of GPS and Glonass Virtual Data for Positioning Under Different Ionospheric Conditions

A Review of Global Navigation Satellite System (GNSS)-Based Dynamic Monitoring Technologies for Structural Health Monitoring

Differential Inter-System Biases Estimation and Initial Assessment of Instantaneous Tightly Combined RTK with BDS-3, GPS, and Galileo

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