Validating and comparing GNSS antenna calibrations

Kallio, Ulla; Koivula, Hannu; Lahtinen, Sonja; Nikkonen, Ville; Poutanen, Markku

doi:10.1007/s00190-018-1134-2

Cited by 19 publications

(13 citation statements)

References 6 publications

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“…The direct comparison of Geo++ and University Bonn IGG products has already been carried out several times by others [ 21 , 41 ], and the authors have shown a very good agreement in L1 patterns and some systematic differences in L2 patterns. This was confirmed in the work of [ 42 ], where the authors also compared these antenna calibrations, but they focused on the estimated position. A two-fold better agreement in the estimated height was derived for the L2 signals rather than the L1 signals.…”

Section: Resultsmentioning

confidence: 55%

Impact of Using GPS L2 Receiver Antenna Corrections for the Galileo E5a Frequency on Position Estimates

Araszkiewicz

Kiliszek

2020

Sensors

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Knowledge of Global Navigation Satellite System (GNSS) antenna phase center variations plays a key role in precise positioning. Proper modeling is achieved by accessing antenna phase center corrections, which are determined in the calibration process. For most receiver antenna types, the International GNSS Service provides such corrections for two GPS and GLONASS carrier signals. In the case of Galileo, access to phase center corrections is difficult; only antennas calibrated in the anechoic chambers have available corrections for Galileo frequencies. Hence, in many of the studies, GPS-dedicated corrections are used for these Galileo frequencies. Differential analysis was conducted in this study to evaluate the impact of such change. In total, 25 stations belonging to the EUREF Permanent Network and equipped with individual calibrated antennas were the subject of this research. The results for both the absolute and relative positioning methods are clear: using GPS L2 corrections for Galileo E5a frequency causes a bias in the estimated height of almost 8 mm. For the horizontal component, a significant difference can be noticed for only one type of antenna.

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Section: Resultsmentioning

confidence: 55%

Impact of Using GPS L2 Receiver Antenna Corrections for the Galileo E5a Frequency on Position Estimates

Araszkiewicz

Kiliszek

2020

Sensors

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“…Therefore, we decided to reduce the GNSS data interval from 15 s to 1 s in the 2016 campaign so that the methods to detect and mitigate the multipath error that were being developed at Space Geodesy and Navigation Laboratory of the University College London (SGNL-UCL), which were described in the previous section, could be better implemented and tested. With regard to the GNSS antennas, we always used the same two 3D choke-ring Leica AR25.R4, which were individually calibrated by the University of Bonn by using the anechoic chamber method [52,53]. The two antennas were set up carefully oriented towards the geodetic north which was rigorously realized by benchmarks that are permanently located near every pillar.…”

Section: Description Of the Experimentsmentioning

confidence: 99%

Development of a Submillimetric GNSS-Based Distance Meter for Length Metrology

García-Asenjo

Baselga

Atkins

et al. 2021

Sensors

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Absolute distance determination in the open air with an uncertainty of a few tenths of a millimetre is increasingly required in many applications that involve high precision geodetic metrology. No matter the technique used to measure, the resulting distances must be proven consistent with the unit of length (SI-metre) as realized in the outdoor facilities traditionally used in length metrology, which are also known as calibration baselines of reference. The current calibration baselines of reference have distances in the range of 10 to 1000 m, but at present there is no solution on the market to provide distances with submillimetric precision in that range. Consequently, new techniques such as multi-wave interferometry, two-wave laser telemeters or laser trackers are being developed. A possible alternative to those sophisticated and expensive techniques is the use of widely used Global Navigation Satellite Systems (GNSS) in order to provide a GNSS-Based Distance Meter (GBDM). The use of a GBDM as a potential technique for length metrology has been thoroughly analysed in several European research projects by using the state-of-the-art geodetic software, such as Bernese 5.2, but no definite conclusions have been drawn and some metrological questions are considered still open. In this paper, we describe a dedicated approach to build up a submillimetric GBDM able to be applied in the current calibration baselines of reference, as well as possible methods to cope with the multipath error of the GNSS signals which is the major limitation for the practical uptaking of the technique in metrology. The accuracy of the proposed approach has been tested following the length metrology standards in four experiments carried out in the Universitat Politècnica de València (UPV). The results demonstrate that the proposed GBDM can provide an accuracy of a few tenths of a millimetre in the current calibration baselines of reference.

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“…The here presented selection of the distribution type and its variability range are solely preliminary steps. At minimum, a GUM analysis of GNSS observations, a much more detailed study of all influencing factors, has to be performed, which is a current project at the Finish Geodetic Institute [12].…”

Section: Approach To Perform a Gum Analysis For Geodetic Observationsmentioning

confidence: 99%

“…The GNSS uncertainty model is just a rough idea, as in general it is difficult to simulate all influence factors with e.g., orbital errors, remaining atmospheric effects, multipath and near field effects. Colleagues from FGI are working on the problem to develop a more realistic GUM model for GNSS, see [12].…”

Section: Input Variables For Gum Analysismentioning

confidence: 99%

Stochastic Properties of Confidence Ellipsoids after Least Squares Adjustment, Derived from GUM Analysis and Monte Carlo Simulations

Niemeier

Tengen²

2020

Mathematics

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In this paper stochastic properties are discussed for the final results of the application of an innovative approach for uncertainty assessment for network computations, which can be characterized as two-step approach: As the first step, raw measuring data and all possible influencing factors were analyzed, applying uncertainty modeling in accordance with GUM (Guide to the Expression of Uncertainty in Measurement). As the second step, Monte Carlo (MC) simulations were set up for the complete processing chain, i.e., for simulating all input data and performing adjustment computations. The input datasets were generated by pseudo random numbers and pre-set probability distribution functions were considered for all these variables. The main extensions here are related to an analysis of the stochastic properties of the final results, which are point clouds for station coordinates. According to Cramer’s central limit theorem and Hagen’s elementary error theory, there are some justifications for why these coordinate variations follow a normal distribution. The applied statistical tests on the normal distribution confirmed this assumption. This result allows us to derive confidence ellipsoids out of these point clouds and to continue with our quality assessment and more detailed analysis of the results, similar to the procedures well-known in classical network theory. This approach and the check on normal distribution is applied to the local tie network of Metsähovi, Finland, where terrestrial geodetic observations are combined with Global Navigation Satellite System (GNSS) data.

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Validating and comparing GNSS antenna calibrations

Cited by 19 publications

References 6 publications

Impact of Using GPS L2 Receiver Antenna Corrections for the Galileo E5a Frequency on Position Estimates

Impact of Using GPS L2 Receiver Antenna Corrections for the Galileo E5a Frequency on Position Estimates

Development of a Submillimetric GNSS-Based Distance Meter for Length Metrology

Stochastic Properties of Confidence Ellipsoids after Least Squares Adjustment, Derived from GUM Analysis and Monte Carlo Simulations

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