Welcome to the UCL GNSS, Geodesy and Navigation Research Group (GNRG)

We are a very active research group in the Department of Civil, Environmental and Geomatic Engineering (CEGE) at University College London. We are part of the Geomatic Engineering research theme which covers the whole breadth of geodesy and geomatics. We are interested in mathematical geodesy; the theory and applications of GNSS and space-based technologies to geodesy; and development and improvement of positioning and navigation technologies. The majority of our current work focuses on Real-Time Kinematic (RTK) precise positioning, orbit modelling, inertial navigation systems, and precise cost-effective positioning and navigation in challenging environments. You can lean more about our work by selecting Research or Publications from the menu opposite. Latest news from the group can be found below.

Resources

 
Guide to Worldwide GNSS Events, Conferences, Workshops, Symposiums, etc.
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Software Programs and Source Code [for non-commercial purposes]
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Activities and Events
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Coming Week's Reading

  Prof. Marek Ziebart presents . . .

"Carrier Phase Ambiguity Resolution for the Global Positioning System Applied to Geodetic Baselines up to 2000 km"


Abstract: "The Global Positioning System (GPS) carrier phase data are biased by an integer number of cycles. A successful strategy has been developed and demonstrated for resolving these integer ambiguities for geodetic baselines of up to 2000 km in length, resulting in a factor of 3 improvement in baseline accuracy, and giving centimeter-level agreement with coordinates inferred by very long baseline interferometry in the western United States. For this experiment, a method using pseudorange data is shown to be more reliable than one using ionospheric constraints for baselines longer than 200 km. An automated algorithm exploits the correlations between the many phase biases of a GPS receiver network to enable the resolution of ambiguities for very long baselines. A method called bias optimizing has been developed, which, unlike traditional bias fixing, does not require an arbitrary confidence test. Bias optimizing is expected . . ."

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