Investigation and Validation of the Time-Varying Characteristic for the GPS Differential Code Bias

Li, Haojun; Xiao, Jingxin; Zhu, Weidong

doi:10.3390/rs11040428

Cited by 11 publications

(9 citation statements)

References 27 publications

(36 reference statements)

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“…To enhance IFCB estimation, the constant part of the GPS IFCB is computed with pseudorange observations [21]. Meanwhile, it has been reported that the GPS pseudorange observation-based IFCB is a combination of satellite DCB(P1-P2) and DCB(P1-P5), while the GPS carrier phasebased IFCB is the combined uncalibrated phase delay [22]:…”

Section: Introductionmentioning

confidence: 99%

Reconstructed estimation method of the multi-frequency GNSS inter-frequency clock bias

Xiao,

Li,

Kang

et al. 2023

Meas. Sci. Technol.

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In consideration of contributions from both carrier phase and pseudorange observations during computation, this study introduces a reconstructed method for estimating the multi-frequency global navigation satellite system (GNSS) inter-frequency clock biases (IFCB). Diverging from conventional approaches that separately calculate the time-varying and constant parts of IFCBs using carrier phase and pseudorange observations, the reconstructed method directly utilizes their combination to estimate satellite IFCBs. To validate the efficacy of the presented approach, 7 d observations from 87 International GNSS Service (IGS) stations are analyzed, with a specific focus on triple-frequency GPS, multi-frequency BDS-3, and Galileo IFCBs, aiming to scrutinize their distinctive characteristics. Furthermore, the performances of satellite IFCB estimation are investigated in both precise point positioning (PPP) and single-point positioning (SPP) using 35 IGS stations over 3 d. The results demonstrate that IFCBs of GPS BLOCK IIIA satellites exhibit centimeter-level variations, distinguishing them from BLOCK IIF counterparts. The Galileo IFCBs vary from millimeter to centimeter level, while those of BDS-3 reach a centimeter level. These variations significantly impact GPS PPP convergence performances but have minimal effects on Galileo and BDS-3 PPP. SPP performances are slightly enhanced when the time-varying IFCB part are taken into account. Additionally, we note disparities in the constant parts of satellite IFCBs computed with differential code bias (DCB) products, reconstructed, and pseudorange observation-based methods, particularly for BDS-3 C2I/C6I-C1P/C5P and C2I/C6I-C1P/C6I combinations. The differences of IFCB estimated with different strategies, SPP and PPP performances show that the reconstructed method is better than others, and the IFCB accuracy decreases when computed with satellite DCB products.

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Section: Introductionmentioning

confidence: 99%

Reconstructed estimation method of the multi-frequency GNSS inter-frequency clock bias

Xiao,

Li,

Kang

et al. 2023

Meas. Sci. Technol.

Self Cite

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show abstract

“…DCBs occur because of hardware imperfections inside receivers and satellites, and are contained in GNSS receiver observations [ 1 ]. However, DCBs must be known for GNSS applications, such as total electron content (TEC) determination, which derives TEC from receiver observations [ 2 , 3 , 4 ]. DCBs can cause errors of several meters in TEC estimates if they are ignored and can even result in negative ionospheric delay values [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Estimation and Analysis of GNSS Differential Code Biases (DCBs) Using a Multi-Spacing Software Receiver

Wang

Zhao

Gao

2021

Sensors

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In the use of global navigation satellite systems (GNSS) to monitor ionosphere variations by estimating total electron content (TEC), differential code biases (DCBs) in GNSS measurements are a primary source of errors. Satellite DCBs are currently estimated and broadcast to users by International GNSS Service (IGS) using a network of GNSS hardware receivers which are inside structure fixed. We propose an approach for satellite DCB estimation using a multi-spacing GNSS software receiver to analyze the influence of the correlator spacing on satellite DCB estimates and estimate satellite DCBs based on different correlator spacing observations from the software receiver. This software receiver-based approach is called multi-spacing DCB (MSDCB) estimation. In the software receiver approach, GNSS observations with different correlator spacings from intermediate frequency datasets can be generated. Since each correlator spacing allows the software receiver to output observations like a local GNSS receiver station, GNSS observations from different correlator spacings constitute a network of GNSS receivers, which makes it possible to use a single software receiver to estimate satellite DCBs. By comparing the MSDCBs to the IGS DCB products, the results show that the proposed correlator spacing flexible software receiver is able to predict satellite DCBs with increased flexibility and cost-effectiveness than the current hardware receiver-based DCB estimation approach.

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“…They reported an agreement with the global ionospheric map (GIM) data with a mean difference of less than 0.7 ns (~2 TECU) and an RMS of less than 0.4 ns (~1 TECU). Li et al [ 17 ] analyzed triple-frequency combinations and proved that the real satellite DCBs are time-varying. Wang et al [ 18 ] mitigated variations in DCB by separating DCB from ambiguity.…”

Section: Introductionmentioning

confidence: 99%

GNSS-Based Non-Negative Absolute Ionosphere Total Electron Content, its Spatial Gradients, Time Derivatives and Differential Code Biases: Bounded-Variable Least-Squares and Taylor Series

Yasyukevich

Mylnikova

Vesnin

2020

Sensors

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Global navigation satellite systems (GNSS) allow estimating total electron content (TEC). However, it is still a problem to calculate absolute ionosphere parameters from GNSS data: negative TEC values could appear, and most of existing algorithms does not enable to estimate TEC spatial gradients and TEC time derivatives. We developed an algorithm to recover the absolute non-negative vertical and slant TEC, its derivatives and its gradients, as well as the GNSS equipment differential code biases (DCBs) by using the Taylor series expansion and bounded-variable least-squares. We termed this algorithm TuRBOTEC. Bounded-variable least-squares fitting ensures non-negative values of both slant TEC and vertical TEC. The second order Taylor series expansion could provide a relevant TEC spatial gradients and TEC time derivatives. The technique validation was performed by using independent experimental data over 2014 and the IRI-2012 and IRI-plas models. As a TEC source we used Madrigal maps, CODE (the Center for Orbit Determination in Europe) global ionosphere maps (GIM), the IONOLAB software, and the SEEMALA-TEC software developed by Dr. Seemala. For the Asian mid-latitudes TuRBOTEC results agree with the GIM and IONOLAB data (root-mean-square was < 3 TECU), but they disagree with the SEEMALA-TEC and Madrigal data (root-mean-square was >10 TECU). About 9% of vertical TECs from the TuRBOTEC estimates exceed (by more than 1 TECU) those from the same algorithm but without constraints. The analysis of TEC spatial gradients showed that as far as 10–15° on latitude, TEC estimation error exceeds 10 TECU. Longitudinal gradients produce smaller error for the same distance. Experimental GLObal Navigation Satellite System (GLONASS) DCB from TuRBOTEC and CODE peaked 15 TECU difference, while GPS DCB agrees. Slant TEC series indicate that the TuRBOTEC data for GLONASS are physically more plausible.

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Investigation and Validation of the Time-Varying Characteristic for the GPS Differential Code Bias

Cited by 11 publications

References 27 publications

Reconstructed estimation method of the multi-frequency GNSS inter-frequency clock bias

Reconstructed estimation method of the multi-frequency GNSS inter-frequency clock bias

Estimation and Analysis of GNSS Differential Code Biases (DCBs) Using a Multi-Spacing Software Receiver

GNSS-Based Non-Negative Absolute Ionosphere Total Electron Content, its Spatial Gradients, Time Derivatives and Differential Code Biases: Bounded-Variable Least-Squares and Taylor Series

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