Inverse modelling of GNSS multipath signals
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 ove...
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ftchalmersuniv:oai:research.chalmers.se:250550 2023-05-15T18:17:46+02:00 Inverse modelling of GNSS multipath signals Strandberg, Joakim 2017 text https://research.chalmers.se/en/publication/250550 unknown https://research.chalmers.se/en/publication/250550 Remote Sensing Oceanography Hydrology Water Resources Signal Processing Geosciences Multidisciplinary GNSS reflectometry sea ice sea level 2017 ftchalmersuniv 2022-12-11T07:01:42Z 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. Other/Unknown Material Sea ice Chalmers University of Technology: Chalmers research |
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Open Polar |
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Chalmers University of Technology: Chalmers research |
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ftchalmersuniv |
language |
unknown |
topic |
Remote Sensing Oceanography Hydrology Water Resources Signal Processing Geosciences Multidisciplinary GNSS reflectometry sea ice sea level |
spellingShingle |
Remote Sensing Oceanography Hydrology Water Resources Signal Processing Geosciences Multidisciplinary GNSS reflectometry sea ice sea level Strandberg, Joakim Inverse modelling of GNSS multipath signals |
topic_facet |
Remote Sensing Oceanography Hydrology Water Resources Signal Processing Geosciences Multidisciplinary GNSS reflectometry sea ice sea level |
description |
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. |
author |
Strandberg, Joakim |
author_facet |
Strandberg, Joakim |
author_sort |
Strandberg, Joakim |
title |
Inverse modelling of GNSS multipath signals |
title_short |
Inverse modelling of GNSS multipath signals |
title_full |
Inverse modelling of GNSS multipath signals |
title_fullStr |
Inverse modelling of GNSS multipath signals |
title_full_unstemmed |
Inverse modelling of GNSS multipath signals |
title_sort |
inverse modelling of gnss multipath signals |
publishDate |
2017 |
url |
https://research.chalmers.se/en/publication/250550 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
https://research.chalmers.se/en/publication/250550 |
_version_ |
1766192942588887040 |