|Title||Automated processing of low-cost GNSS receiver data|
Lachapelle, G; Ghoddousi-Fard, R; Gratton, P|
|Source||Proceedings 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|
|Alt Series||Natural Resources Canada, Contribution Series 20190180|
|Publisher||Institute of Navigation|
|Meeting||ION GNSS+ 2019 - 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation; Miami, FL; US; September 16-20, 2019|
|File format||html; pdf (Adobe® Reader®)|
|Subjects||geophysics; Science and Technology; satellite geodesy; ionosphere; models; Canadian Spatial Referencing System; global navigation satellite systems (GNSS); Data processing; Quality control; Geographic
|Illustrations||tables; photographs; plots; bar graphs; time series; location maps|
|Program||Geodetic Survey Canadian
Spatial Reference System|
|Released||2019 09 01|
|Abstract||The 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
|Summary||(Plain Language Summary, not published)|
This article discusses the challenges and solutions in using data from smartphones and tablets for Global Navigation Satellite System (GNSS) data
processing. When we use low-cost GNSS chips and antennas on these devices, the data can become noisy and unreliable. The researchers aimed to find ways to process this data accurately.
The study's main goal was to adapt existing data processing
methods for high-end GNSS receivers to work well with cheaper devices. They made several key changes to the processing methods:
They used a new way to measure the quality of the data, which is different from what's used for high-end
They improved the corrections for the ionosphere, a layer in the Earth's atmosphere, and added quality indicators to these corrections.
They didn't estimate the atmospheric conditions in the same way as with high-end devices, and
instead used more accurate models.
They changed the way they checked the data's quality.
The researchers found that by making these changes, they could achieve very accurate results when processing data from smartphones, reaching
centimeter-level accuracy under good conditions.
The scientific impact of this work is significant because it helps make GNSS data from low-cost devices more reliable and accurate. This is important because many people use smartphones and tablets
for navigation, and better data processing means better accuracy in location services, which can benefit various applications from personal navigation to precision agriculture.