Characterising the GNSS correlation function using a high gain antenna and long coherent integration&amp;#x2014;Application to signal quality monitoring

Lestarquit, Laurent; Gregoire, Yoan; Thevenon, Paul

doi:10.1109/plans.2012.6236830

Cited by 19 publications

(10 citation statements)

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“…As was suggested in the previous section, the receiver originating code biases are sometimes also dependent on the transmitting satellite. This is the case when the transmitted signals are distorted at the satellite payload (Edgar et al 1999;Lestarquit et al 2012). As the receiver biases of both signals are constituents of the DCB, this effect of satellite dependence will also show up on the receiver DCB under the conditions mentioned above.…”

Section: Differential Code Biasesmentioning

confidence: 99%

“…Depending on the correlator spacing adopted in the receiver design, signal deformations on L1 originating from the SVN 19 hardware gave rise to different internal delays in the receivers, resulting in a differential positioning error of several meters when the reference and the rover receiver used different correlator spacing in their discriminators. Recent findings by Lestarquit et al (2012) showed delay differences as large as 0.7 m between using a 0.1 and 0.05 chip discriminator when analyzing distortions on the C/Acode transmitted from GPS Block IIA PRN-32, corresponding to SVN 23. It was also shown that different satellites, which exhibit different kinds of distortions on their signals, produced different delays for a given correlator spacing.…”

Section: Satellite Dependency Of Receiver Originating Biasesmentioning

confidence: 99%

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Review of code and phase biases in multi-GNSS positioning

et al. 2016

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A review of the research conducted until present on the subject of Global Navigation Satellite System (GNSS) hardware-induced phase and code biases is here provided. Biases in GNSS positioning occur because of imperfections and/or physical limitations in the GNSS hardware. The biases are a result of small delays between events that ideally should be simultaneous in the transmission of the signal from a satellite or in the reception of the signal in a GNSS receiver. Consequently, these biases will also be present in the GNSS code and phase measurements and may there affect the accuracy of positions and other quantities derived from the observations. For instance, biases affect the ability to resolve the integer ambiguities in Precise Point Positioning (PPP), and in relative carrier phase positioning when measurements from multiple GNSSs are used. In addition, code biases affect ionospheric modeling when the Total Electron Content is estimated from GNSS measurements. The paper illustrates how satellite phase biases inhibit the resolution of the phase ambiguity to an integer in PPP, while receiver phase biases affect multi-GNSS positioning. It is also discussed how biases in the receiver channels affect relative GLONASS positioning with baselines of mixed receiver types. In addition, the importance of code biases between signals modulated onto different carriers as is required for modeling the ionosphere from GNSS measurements is discussed. The origin of biases is discussed along with their effect on GNSS positioning, and descriptions of how biases can be estimated or in other ways handled in the positioning process are provided.

QC 20170922

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Section: Differential Code Biasesmentioning

confidence: 99%

Section: Satellite Dependency Of Receiver Originating Biasesmentioning

confidence: 99%

Review of code and phase biases in multi-GNSS positioning

et al. 2016

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QC 20170922

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“…Satellite Navigation https://satellite-navigation.springeropen.com/ *Correspondence: hechengyan@ntsc.ac.cn 1 National Time Service Center, Chinese Academy of Sciences, No. 3 Shuyuan East Road, Lintong, Xi'an 710600, Shaanxi, China Full list of author information is available at the end of the article a signal deformation of the GPS space vehicle number (SVN) 19 in 1993, which led to a significant degradation of the PNT performance when used for a differential resolution, numerous publications have mainly concentrated on the characterization of satellite signal deformations [7][8][9][10][11][12][13]. Research results have shown that receivers with different configurations, such as a different correlator spacing or different frontend bandwidth, exhibit inconsistent pseudorange biases to individual signal deformations for a GPS satellite [14].…”

Section: Open Accessmentioning

confidence: 99%

Initial analysis for characterizing and mitigating the pseudorange biases of BeiDou navigation satellite system

Guo

et al. 2020

Satell Navig

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Pseudorange bias has become a practical obstacle in the field of high-precision global navigation satellite system (GNSS) applications, which greatly restricts the further development of high-precision applications. Unfortunately, no studies have been conducted on the pseudorange biases of the BeiDou navigation satellite system (BDS). To mitigate the effects of pseudorange biases on the BDS performance to the greatest extent possible, the origin of such BDS pseudorange biases are first thoroughly illustrated, based upon which the dependency of the biases on the receiver configurations are studied in detail. Owing to the limitations regarding the parameter re-settings for hardware receivers, software receiver technology was used to achieve the ergodicity of the receiver parameters, such as the correlator spacing and front-end bandwidth, using high-fidelity signal observations collected by a 40-m-high gain dish antenna at Haoping Observatory. Based on this, the pseudorange biases of the BDS B1I and B3I signals and their dependency on different correlator spacings and front-end bandwidths were adequately provided. Finally, herein, the suggested settings of the correlator spacing and front-end bandwidth for BDS receivers are in detail proposed for the first time. As a result, the pseudorange biases of the BDS signals will be less than 20 cm, reaching even under 10 cm, under this condition. This study will provide special attention to GNSS pseudorange biases, and will significantly promote a clear definition of the appropriate receiver parameter settings in the interface control documents of BDS and other individual satellite systems.

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“…Considering that the symbol rate of B1C data signal is 100 sps and primary code period is 10 ms [18], 10-ms-long coherent integration time is adopted in the receiver. Similar to chip domain method and Vision Correlator [28], longer coherent integration time can further average the noise [29]. Table 1 describes the receiver parameters in the experiments.…”

Section: ) Configuration Of the Receivermentioning

confidence: 99%

Detection and Classification of GNSS Signal Distortions Based on Quadratic Discriminant Analysis

et al. 2020

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Satellite signal distortions, or so called ''evil waveforms'' (EWFs), may cause severe distortions of the cross-correlation function in receivers. Undetected EWFs could result in large range errors and threaten the integrity of Global Navigation Satellite System (GNSS). Analog distortion and digital distortion are two classical types of signal distortions. With the advent of modernized GNSS signals, more failure types are considered to cover potential EWFs of Binary offset carrier (BOC) modulated signals, such as codeonly distortion and subcarrier-only distortion. Different failure types affect the correlation functions and the tracking precision in different ways. Therefore, it is useful to identify the failure type of a detected distortion. Conventional multi-correlator method can detect signal distortions; however, it is infeasible to identify the failure types. We developed a novel multi-correlator method based on Quadratic Discriminant Analysis (QDA). The QDA-based method also uses correlation-domain detection metrics but performs distortion type classification by supervised learning algorithm. Experimentally measured results on Beidou B1C data signals are presented, which show the effectiveness and robustness of proposed method. Compared with conventional multi-correlator method, the QDA-based signal quality monitoring (SQM) method shows better performance on detecting EWFs and provides an extra capability to identify the failure types accurately. INDEX TERMS Evil waveforms (EWFs), quadratic discriminant analysis (QDA), signal quality monitoring (SQM), multi-correlator method.

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Characterising the GNSS correlation function using a high gain antenna and long coherent integration—Application to signal quality monitoring

Cited by 19 publications

References 0 publications

Review of code and phase biases in multi-GNSS positioning

Review of code and phase biases in multi-GNSS positioning

Initial analysis for characterizing and mitigating the pseudorange biases of BeiDou navigation satellite system

Detection and Classification of GNSS Signal Distortions Based on Quadratic Discriminant Analysis

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