Inverse modelling of GNSS multipath signals
Licentiate thesis, 2017

Measuring the world around us is necessary to observe and understand the changes that occur in our environment. A widely distributed network of measurement stations can help us to understand ongoing and predict future climate change. GNSS reflectometry has the capacity of providing data from all over the world, as there are already many GNSS stations established and operated for navigational and meteorological purposes. This thesis presents a new way of retrieving environmental data from GNSS signal-to-noise ratio measurements which has the capability to provide new types of measurements. The method is based on inverse modelling of the signal-to-noise ratio in order to retrieve physical parameters of reflecting surfaces around GNSS installations. It is successfully demonstrated that the method improves the precision of the GNSS reflectometry derived sea surface height measurements significantly. By using the signal-to-noise ratio pattern, it is also — for the first time — demonstrated that it is possible to use GNSS reflectometry to detect coastal sea ice.

GNSS

reflectometry

sea ice

sea level

EC-lecture hall, Hörsalsvägen 11, Chalmers
Opponent: Prof. Kristine Larson, University of Colorado, US

Author

Joakim Strandberg

Chalmers, Earth and Space Sciences, Onsala Space Observatory

Inverse modelling of GNSS multipath for sea level measurements - initial results

Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS) Volume 2016-November, 1 November 2016, Article number 7729479, Pages 1867-1869 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016; Beijing; China; 10 - 15 July 2016,; Vol. 2016-November(2016)p. 1867-1869

Paper in proceedings

Improving GNSS-R sea level determination through inverse modeling of SNR data

Radio Science,; Vol. 51(2016)p. 1286-1296

Journal article

Strandberg, J., Hobiger, T., and Haas, R. Coastal sea ice detection using ground-based GNSS-R

Subject Categories

Remote Sensing

Oceanography, Hydrology, Water Resources

Signal Processing

Geosciences, Multidisciplinary

Infrastructure

Onsala Space Observatory

Publisher

Chalmers University of Technology

EC-lecture hall, Hörsalsvägen 11, Chalmers

Opponent: Prof. Kristine Larson, University of Colorado, US