A study on the dependency of GNSS pseudorange biases on correlator spacing

Hauschild, André; Montenbruck, Oliver

doi:10.1007/s10291-014-0426-0

Cited by 91 publications

(50 citation statements)

References 7 publications

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“…For the sake of conciseness, the figures showing the estimated satellites and receivers DCBs are not presented. Table 3 compares for three Galileo IOV (in-orbit validation) satellites, the DCBs estimated using the DCB_FIX software with the manufacturer measured DCBs that have recently been published by the European Space Agency (ESA) on its website (Galileo 2016). Note that these published values for IOVs are based on absolute calibration carried out on ground against a payload verification system.…”

Section: Results For Estimated Satellites and Receivers Dcbs Using Nementioning

confidence: 99%

Estimation and analysis of multi-GNSS differential code biases using a hardware signal simulator

et al. 2018

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(2018) Estimation and analysis of multi-GNSS differential code biases using a hardware signal simulator. GPS Solutions, 22 . 32/1-32/12. ISSN 1521-1886 Access from the University of Nottingham repository: http://eprints.nottingham.ac.uk/49019/9/s10291-018-0700-7.pdf Copyright and reuse:The Nottingham ePrints service makes this work by researchers of the University of Nottingham available open access under the following conditions. This article is made available under the Creative Commons Attribution licence and may be reused according to the conditions of the licence. For more details see: http://creativecommons.org/licenses/by/2.5/ A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. AbstractIn ionospheric modeling, the differential code biases (DCBs) are a non-negligible error source, which are routinely estimated by the different analysis centers of the International GNSS Service (IGS) as a by-product of their global ionospheric analysis. These are, however, estimated only for the IGS station receivers and for all the satellites of the different GNSS constellations. A technique is proposed for estimating the receiver and satellites DCBs in a global or regional network by first estimating the DCB of one receiver set as reference. This receiver DCB is then used as a 'known' parameter to constrain the global ionospheric solution, where the receiver and satellite DCBs are estimated for the entire network. This is in contrast to the constraint used by the IGS, which assumes that the involved satellites DCBs have a zero mean. The 'known' receiver DCB is obtained by simulating signals that are free of the ionospheric, tropospheric and other group delays using a hardware signal simulator. When applying the proposed technique for Global Positioning System legacy signals, mean offsets in the order of 3 ns for satellites and receivers were found to exist between the estimated DCBs and the IGS published DCBs. It was shown that these estimated DCBs are fairly stable in time, especially for the legacy signals. When the proposed technique is applied for the DCBs estimation using the newer Galileo signals, an agreement at the level of 1-2 ns was found between the estimated DCBs and the manufacturer's measured DCBs, as published by the European Space Agency, for the three still operational Galileo in-orbit validation satellites.

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Section: Results For Estimated Satellites and Receivers Dcbs Using Nementioning

confidence: 99%

Estimation and analysis of multi-GNSS differential code biases using a hardware signal simulator

et al. 2018

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“…Our analysis suggests that the magnitude of ROTIs differs among the four receiver types, that is, Javad, Leica, Septentrio, and Trimble. Those types of GNSS receivers have their own configurations, such as tracking techniques (Hauschild & Montenbruck, ; Humphreys et al, ). We speculate that the diverse tracking techniques adopted by various GNSS receivers contribute to the inconsistency of multi‐GNSS ROTI.…”

Section: Discussionmentioning

confidence: 99%

On Inconsistent ROTI Derived From Multiconstellation GNSS Measurements of Globally Distributed GNSS Receivers for Ionospheric Irregularities Characterization

Liu

Yang

et al. 2019

Radio Science

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This study for the first time presents an investigation into the exploitation of multi‐Global Navigation Satellite System (GNSS) observations (Global Positioning System, Globalnaya Navigazionnaya Sputnikovaya Sistema, Galileo, and BeiDou) to characterize ionospheric plasma irregularities, based on the rate of change of total electron content index (ROTI) sampled at 1 s. It is demonstrated that the multi‐GNSS ROTIs can represent temporal evolutions of ionospheric plasma irregularities during a large geomagnetic storm. However, an inconsistency in the magnitudes of multi‐GNSS ROTIs is found among a variety of GNSS receivers (i.e., Javad, Leica, Trimble, and Septentrio). Through cross comparisons between GNSS receivers installed at zero/short baselines and validations by receivers distributed separately, it is observed that the magnitude of ROTI corresponding to each system differs between closely installed and even collocated receivers of different models, as well as between the GNSS signals on the same frequency. From 1‐year (i.e., 2015) data analysis, it is found that the magnitudes of multi‐GNSS ROTIs exhibit the dependence on the receiver type. Among the four GNSS receiver types, the largest discrepancy in the multi‐GNSS ROTIs is observed from Septentrio receivers, while the smallest one is shown by Trimble receivers. A one‐to‐one comparison indicates that the ROTI difference is noticeable and can increase to 4–6 TECu/min under ionospheric irregularities conditions, that is, in the postsunset period of 18–02 local time. To investigate the inconsistency, the effect of adopting different equivalent noise bandwidths in the tracking loop design is discussed, via cross comparisons between a GNSS software‐defined receiver and a collocated Septentrio receiver. The result shows that adopting 15‐ and 2‐Hz noise bandwidths in the tracking loop can cause 0.2‐ to 0.5‐TECu/min differences in the magnitude of ROTI, suggesting that the diverse tracking techniques deployed by various receivers are very likely a major contributor to the inconsistency of multi‐GNSS ROTIs.

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“…The estimates from the GRC, CODE, and ESA agreed well for most satellites, but the R10(−7), R13(−2), R16(−1), and R15(0) exhibited apparent discrepancies. These discrepancies might have been caused by the different types of receivers that had an impact on the satellite DCBs estimation [42]. Only the CODE and ESA had used both the GPS and GLONASS data in order to establish their global ionosphere models during this period.…”

Section: Gps and Glonass Satellite Dcbs Validation With Iaacs Dcbsmentioning

confidence: 99%

Global Ionosphere Mapping and Differential Code Bias Estimation during Low and High Solar Activity Periods with GIMAS Software

Zhang

Zhao

2018

Remote Sensing

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Abstract:Ionosphere research using the Global Navigation Satellite Systems (GNSS) techniques is a hot topic, with their unprecedented high temporal and spatial sampling rate. We introduced a new GNSS Ionosphere Monitoring and Analysis Software (GIMAS) in order to model the global ionosphere vertical total electron content (VTEC) maps and to estimate the GPS and GLObalnaya NAvigatsionnaya Sputnikovaya Sistema (GLONASS) satellite and receiver differential code biases (DCBs). The GIMAS-based Global Ionosphere Map (GIM) products during low (day of year from 202 to 231, in 2008) and high (day of year from 050 to 079, in 2014) solar activity periods were investigated and assessed. The results showed that the biases of the GIMAS-based VTEC maps relative to the International GNSS Service (IGS) Ionosphere Associate Analysis Centers (IAACs) VTEC maps ranged from −3.0 to 1.0 TECU (TEC unit) (1 TECU = 1 × 10 16 electrons/m 2 ). The standard deviations (STDs) ranged from 0.7 to 1.9 TECU in 2008, and from 2.0 to 8.0 TECU in 2014. The STDs at a low latitude were significantly larger than those at middle and high latitudes, as a result of the ionospheric latitudinal gradients. When compared with the Jason-2 VTEC measurements, the GIMAS-based VTEC maps showed a negative systematic bias of about −1.

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A study on the dependency of GNSS pseudorange biases on correlator spacing

Cited by 91 publications

References 7 publications

Estimation and analysis of multi-GNSS differential code biases using a hardware signal simulator

Estimation and analysis of multi-GNSS differential code biases using a hardware signal simulator

On Inconsistent ROTI Derived From Multiconstellation GNSS Measurements of Globally Distributed GNSS Receivers for Ionospheric Irregularities Characterization

Global Ionosphere Mapping and Differential Code Bias Estimation during Low and High Solar Activity Periods with GIMAS Software

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