Articles | Volume 9, issue 3
https://doi.org/10.5194/amt-9-1303-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-9-1303-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
30 Mar 2016
Research article |
| 30 Mar 2016
Statistical framework for estimating GNSS bias
Juha Vierinen
CORRESPONDING AUTHOR
Haystack Observatory, Massachusetts Institute of Technology, Route 40 Westford, 01469 MA, USA
Anthea J. Coster
Haystack Observatory, Massachusetts Institute of Technology, Route 40 Westford, 01469 MA, USA
William C. Rideout
Haystack Observatory, Massachusetts Institute of Technology, Route 40 Westford, 01469 MA, USA
Philip J. Erickson
Haystack Observatory, Massachusetts Institute of Technology, Route 40 Westford, 01469 MA, USA
Johannes Norberg
Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland
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- Early Morning Equatorial Ionization Anomaly From GOLD Observations F. Laskar et al. 10.1029/2019JA027487
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- Validating Ionospheric Models Against Technologically Relevant Metrics A. Chartier et al. 10.1029/2023SW003590
- An Ionosphere Specification Technique Based on Data Ingestion Algorithm and Empirical Orthogonal Function Analysis Method E. Aa et al. 10.1029/2018SW001987
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- Traveling Ionospheric Disturbances in the Vicinity of Storm‐Enhanced Density at Midlatitudes S. Zhang et al. 10.1029/2022JA030429
- GCP and PPK Utilization Plan to Deal with RTK Signal Interruption in RTK-UAV Photogrammetry J. Cho & B. Lee 10.3390/drones7040265
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Saved (preprint)
Latest update: 26 Jul 2024
Short summary
We present a statistical framework for estimating GNSS receiver bias by using a weighted linear least squares of independent differences (WLLSID) model to examine differences of a large number of TEC measurements. This allows a consistent way for treating elevation-dependent model errors and spatiotemporal distance-dependent geophysical differences arising in ionospheric GNSS measurements. The method is also applicable to other GNSS system than GPS, supporting, e.g., GLONASS.
We present a statistical framework for estimating GNSS receiver bias by using a weighted linear...
