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Application of Neural Network to GNSS-R Wind Speed Retrieval.
- Source :
-
IEEE Transactions on Geoscience & Remote Sensing . Dec2019, Vol. 57 Issue 12, p9756-9766. 11p. - Publication Year :
- 2019
-
Abstract
- This paper applies a machine learning (ML) algorithm based on the multi-hidden layer neural network (MHL-NN) for ocean surface wind speed estimation using global navigation satellite system (GNSS) reflection measurements. Unlike conventional wind speed retrieval methods that often depend on limited scalar delay-Doppler map (DDM) observables, the proposed MHL-NN makes use of information captured by the entire DDM. Both simulated and real data sets are used to train and evaluate the performance of the MHL-NN and compare it to a conventional wind speed retrieval method and other prevailing ML algorithms. The results show that the MHL-NN algorithm outperforms the other methods in terms of the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the wind speed estimation. [ABSTRACT FROM AUTHOR]
- Subjects :
- *WIND speed
*GLOBAL Positioning System
*STANDARD deviations
Subjects
Details
- Language :
- English
- ISSN :
- 01962892
- Volume :
- 57
- Issue :
- 12
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Geoscience & Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 141052396
- Full Text :
- https://doi.org/10.1109/TGRS.2019.2929002