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International Journal of Antennas and Propagation
Volume 2016 (2016), Article ID 2706521, 8 pages
Research Article

Wind Speed Inversion in High Frequency Radar Based on Neural Network

1School of Electronic Information, Wuhan University, Wuhan 430072, China
2Institute of Marine and Coastal Sciences, Rutgers University, New Brunswick, NJ 08901, USA

Received 6 April 2016; Accepted 14 August 2016

Academic Editor: Khalid El-Darymli

Copyright © 2016 Yuming Zeng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Wind speed is an important sea surface dynamic parameter which influences a wide variety of oceanic applications. Wave height and wind direction can be extracted from high frequency radar echo spectra with a relatively high accuracy, while the estimation of wind speed is still a challenge. This paper describes an artificial neural network based method to estimate the wind speed in HF radar which can be trained to store the specific but unknown wind-wave relationship by the historical buoy data sets. The method is validated by one-month-long data of SeaSonde radar, the correlation coefficient between the radar estimates and the buoy records is 0.68, and the root mean square error is 1.7 m/s. This method also performs well in a rather wide range of time and space (2 years around and 360 km away). This result shows that the ANN is an efficient tool to help make the wind speed an operational product of the HF radar.