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TitleAutomated processing of low-cost GNSS receiver data
 
AuthorBanville, SORCID logo; Lachapelle, G; Ghoddousi-Fard, R; Gratton, P
SourceProceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019; 2019 p. 3636-3652, https://doi.org/10.33012/2019.16972
Image
Year2019
Alt SeriesNatural Resources Canada, Contribution Series 20190180
PublisherInstitute of Navigation
MeetingION GNSS+ 2019 - 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation; Miami, FL; US; September 16-20, 2019
Documentbook
Lang.English
Mediaon-line; digital
File formathtml; pdf (Adobe® Reader®)
Subjectsgeophysics; Science and Technology; satellite geodesy; ionosphere; models; Canadian Spatial Referencing System; global navigation satellite systems (GNSS); Data processing; Quality control; Geographic data
Illustrationstables; photographs; plots; bar graphs; time series; location maps
ProgramGeodetic Survey Canadian Spatial Reference System
Released2019 09 01
AbstractThe availability of raw observations from smartphones and tablets brings new challenges to GNSS data processing. Low-cost GNSS chipsets, combined with omnidirectional antennas, can lead to measurements highly contaminated by noise and multipath. Therefore, data quality depends not only on the device but also on the environment. Such a diversity is complex to handle for automated GNSS data processing services such as the NRCan precise point positioning (PPP) service. Processing strategies developed for geodetic receivers now require adaptations to be suitable for low-cost devices: 1) carrier-to-noise weighting should replace elevation-dependent weighting; 2) precise ionospheric corrections with meaningful quality indicators should be available; 3) the residual tropospheric zenith delay parameter should not be estimated in the PPP filter, which calls for more accurate a priori tropospheric models; and 4) quality control algorithms should rely on geometry-based rather than geometry-free approaches. With such modifications, static PPP solutions using data collected with a Huawei Mate 20X smartphone can converge to cm-level accuracies under favorable signal tracking conditions.
GEOSCAN ID315348

 
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