A Novel Method for Ocean Wind Speed Detection Based on Energy Distribution of Beidou Reflections.

The Global Navigation Satellite System Reflectometry (GNSS-R) technique exploits the characteristics of reflected GNSS signals to estimate the geophysical parameters of the earth's surface. This paper focuses on investigating the wind speed retrieval method using ocean scattered signals from a Beidou Geostationary Earth Orbit (GEO) satellite. Two new observables are proposed by computing the ratio of the low energy zone and the high energy zone of the delay waveform. Coastal experimental raw data from a Beidou GEO satellite are processed to establish the relationship between the energy-related observables and the sea surface wind. When the delay waveform normalized amplitude (this will be referred to as "threshold" in what follows) is 0.3, fitting results show that the coefficient of determination is more than 0.76 in the gentle wind scenario (<10 m/s), with a root mean square error (RMSE) of less than 1.0 m/s. In the Typhoon UTOR scenario (12.7 m/s~37.3 m/s), the correlation level exceeds 0.82 when the threshold is 0.25, with a RMSE of less than 3.10 m/s. Finally, the impact of the threshold and coherent integration time on wind speed retrieval is discussed to obtain an optimal result. When the coherent integration time is 50 milliseconds and the threshold is 0.15, the best wind speed retrieval error of 2.63 m/s and a correlation level of 0.871 are obtained in the UTOR scenario.

A Novel Wind Speed Estimation Based on the Integration of an Artificial Neural Network and a Particle Filter Using BeiDou GEO Reflectometry.

Oceanographic remote sensing, which is based on the sensitivity of reflected signals from the Global Navigation Satellite Systems (GNSS), so-called GNSS-Reflectometry (GNSS-R), is very useful for the observation of ocean wind speed. Wind speed estimation over the ocean is the core factor in maritime transportation management and the study of climate change. The main concept of the GNSS-R technique is using the different times between the reflected and the direct signals to measure the wind speed and wind direction. Accordingly, this research proposes a novel technique for wind speed estimation involving the integration of an artificial neural network and the particle filter based on a theoretical model. Moreover, particle swarm optimization was applied to find the optimal weight and bias of the artificial neural network, in order to improve the accuracy of the estimation result. The observation dataset of the reflected signal information from BeiDou Geostationary Earth Orbit (GEO) satellite number 4 was used as an input for the estimation model. The data consisted of two phases with I and Q components. Two periods of BeiDou data were selected, the first period was from 3 to 8 August 2013 and the second period was from 12 to 14 August 2013, which corresponded to events from the typhoon Utor. The in situ wind speed measurement collected from the buoy station was used to validate the results. A coastal experiment was conducted at the Yangjiang site located in the South China Sea. The results show the ability of the proposed technique to estimate wind speed with a root mean square error of approximately 1.9 m/s.