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

Abstract

(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.

Absolute calibration of GNSS timing stations and its applicability to real signals

“…For the Galileo signals we can observe a global mean bias of −1.32 or −1.65 ns (depending on the receiver) for the DCB E1-E5a, −1.36 ns for the DCB E1-E5b and −0.40 ns for the DCB E1-E6. A similar estimation, limited to IOV satellites, was proposed in [31] for E1-E5a, and their results show a bias of −1.37 ns, i.e. in excellent agreements with ours.…”

A Multi-Frequency Galileo PPP-RTK Convergence Analysis with an Emphasis on the Role of Frequency Spacing

“…As a result, the satellite DCBs between E5b and E5 signals are almost zero, as also shown in Li et al [50]. The only exception to this case is satellite E24, for which a code bias of larger magnitude is seen, while high differential biases have also been reported earlier in Ammar et al [51]. In the case of E6, we observe that the satellite code biases do not fluctuate within a narrow range, unlike E5b and E5, indicating that the satellite DCBs between E6 and the other signals are not equal to zero.…”

Quality monitoring and biases estimation of BOC navigation signals

“…The abnormities of navigation signals are usually reflected in signal power, spectrum, modulation error, correlation performance, code/carrier tracking performance, ranging performance [28,29], etc. Accordingly, the evaluation indicators of SQM are mainly power spectrum, eye diagram, correlation peak, consistency and other waveforms, as well as carrier-to-noise ratio, amplitude modulation imbalance, carrier phase deviation, correlation loss, S curve deviation, and so on [30].…”