## 1 Introduction  
Global Navigation Satellite Systems (GNSS) technologies are widely used for positioning, navigation, and time applications based on information from direct signals. In addition, the reflected signals from satellites over the land or ocean surface can be applied for Earth observations known as GNSS-reflectometry (GNSS-R). This is a well-established technique for remote sensing that is used in many applications to observe the geophysical parameters of the Earth. The first bistatic radar remote sensing technique was proposed for an ocean altimetry application, based on the L-band of Global Positioning System (GPS) signal, in 1993 by the European Space Agency [1]. Several platforms have been widely discussed and developed to capture GNSS-R information, such as space-borne, satellite, and ground stations. It is highly effective for monitoring the Earth's environments, measuring significant wave heights, wind speeds, soil moisture, and ice and snow thicknesses [2; 3; 4; 5]. In particular, the characteristics of wind speed are an important factor for the observations of typhoon and hurricane events [6; 7]. In 2003, Surrey Satellite Technology developed the first GPS-R receiver onboard the UK-Disaster Monitoring Constellation (UK-DMC) satellite in order to establish ocean and land surface conditions [8]. Recently, the Cyclone Global Navigation Satellite System (CYGNSS) project was developed by the National Aeronautics and Space Administration (NASA) to predict the occurrences and observe the characteristics of hurricanes. CYGNSS consists of eight micro-satellites launched into a low Earth orbit with a 35 degree inclination. Each satellite receives information from four GPS satellites [9; 10]. The main advantage of this technique is the large number of navigation satellites in space, such as the GPS, GLONASS, Galileo, and BeiDou satellite constellations, which provide more information and a greater prominent spatial-temporal sampling capability over the Earth's surface. Moreover, the benefit of long-term ubiquitous information that is freely available provides the opportunity to achieve global coverage at a lower cost compared to an active instrument. The Chinese BeiDou navigation satellite system (BDS) has already provided excellent performance in terms of precise positioning, Total Electron Content (TEC) estimation, and modeling of the Earth's ionosphere, etc. principally in the Asia-Pacific region [11; 12]. As of July 2017, BeiDou consists of five Geostationary Earth Orbit (GEO), six Inclined Geo-Synchronization Orbiters (IGSO), and three Medium Earth Orbiters (MEO). Furthermore, by 2020, BeiDou constellations will fully support global full-time and full-weather, which can be applied in many applications, especially for short message communication services. In the same way, the information from reflected signals was studied in many remote sensing applications [13; 14; 15; 16]. From 12 to 14 August 2013, the typhoon Utor formed in the Eastern part of the Philippines and dissipated in Southern China. Figure 1 shows the tracking map of the typhoon Utor simulated from wind speed information from the Japan Meteorological Agency. Consequently, during the typhoon period, this paper estimated wind speeds using the dataset from the BeiDou satellite. In general, wind speed estimation based on reflected signals has been developed and discussed. For instance, Least Squares (LS) fitting of the measured Delay Doppler Map (DDM) to the theoretical Zavorotny and Voronvich model and the regression technique based on geophysical parameters. Both techniques have been widely applied and the results show good agreement with in situ wind speed measurement [17; 18; 19; 20; 21; 22; 23]. However, this technique does not learn the characteristics of the variables. For this reason, a machine learning technique was applied to estimate wind speeds [24].