Coastal ocean wind speed estimation based GNSS-reflectometry of beidou GEO satellite

Global Navigation Satellite System-Reflectometry (GNSS-R) is useful for the ocean remote sensing. It has shown promising results as altimetry, Significant Wave Height, and wind speed measurement. To retrieve the information of the ocean surface, the GNSS-R technique receive the direct signal from GNSS satellites and capture the reflected signal by GNSS-R receivers. The difference delay of the reflected signal provided difference characteristics of the ocean including wind speed information. The aim of this paper is to estimate the ocean wind speed in the coastal area using the reflected signal information. This paper used the observed data sets from 3 to 12 January 2014 collected from the Geostationary Earth Orbit (GEO) of Chinese satellite (BeiDou G1) which consist of phase I and Q component and the in situ wind speed measurement collected from buoy station. A Method based on Artificial Neural Network (ANN) technique for wind speed estimation was presented. In addition, Particle Filter (PF) based autoregressive model was used to improve the efficacy of ANN. The performance of proposed technique has evaluated by using the Root Mean Square Error (RMSE) as shown in the experimental result section.