Short-Term wind speed estimation based on kernel density estimation using GNSS-reflectometry observation data

Ocean remote sensing based satellite image is useful for the Earth observation such as altimetry, Significant Wave Height, and wind speed measurement. However, The Global Navigation Satellite System (GNSS) represents the new challenge using special feature of the reflected signal to observe characteristics of the ocean call GNSS - reflectometry. The advantages of this technique are that using the same signal with navigation system and low cost. The peak of amplitudes from the reflected signal are used to observe data sets consist of phase I and Q from the Geostationary Earth Orbit (GEO) of Chinese satellite (BeiDou G1), the data are acquired on 4 days from 3 - 4 January 2014 for the training data and 7 - 8 January 2014 for the testing data. This paper proposes the Kernel Density Estimation (KDE) approach specific on the Gaussian kernel to model the static nonlinear input-output relationship for wind speed estimation. This technique has robust to noise from observation environment. In order to improve the efficiency of KDE approach, which depends on the bandwidth, this paper introduces the Particle Swarm Optimization (PSO) technique to find the optimal bandwidth of KDE approach since PSO is a population based stochastic approach widely used to solve an optimal problem in the search space. The experimental result section shows the efficiency of the proposed method by compare the error with the regression technique and the KDE approach based rules of thumb.