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Ocean, both gorgeous and enigmatic, strongly attracted generation after generation of people to explore the use of research and development, has become an important topic in the world today, to provide broad prospects for the survival and development of the human will. Marine area accounts for about two-thirds of the earth's surface, the rich marine resources of all kinds. The 21st century will be the century of ocean development. One of the important symbol of this new century is the marine technology, especially the development of marine high-tech. However, the marine environment is extremely complex, wind, wave, flow, tidal and other elements of the physical ocean data and its variation have very important implications for the national defense, shipping, shipbuilding, ports and offshore oil platform construction. X-band navigation radar can not only be used to monitor a moving target at sea, measuring waves and currents, and sea surface wind field can be extracted. The X-band radar economy, real-time and convenient features relative to other tools, you can easily weather monitoring near the ocean dynamic environmental elements. The paper first describes the sea surface wind field extraction method and the current research, and then summarized extracted wind field of multi-sensor, through analysis and comparison, has a certain degree of similarity in the algorithm on the principles and methods to extract multi-sensor wind field system has important reference value for further sea surface wind field of scientific research. The author introduces the X-band radar algorithm to extract wind farm and related knowledge as well as the conduct of the trial, which is the main content of this paper. The algorithm is divided into two, one is based on the method of optical flow motion estimation technology to extract wind farm, and the other is a local gradient method to extract the wind direction and the neural network extracted wind speed. This highlights is the former, this new technology does not require calibration phase of the radar system is still relatively good, most of the results from the test results, confirming the enforceability of this algorithm. However, because the test data is rich enough and coastal waters inhomogeneity factors exist, there are some errors, the need for further study to eliminate; For the latter, mainly follows the local gradient algorithm for synthetic aperture radar inversion wind direction . However, due to the time pressed for the lack of data, did not ultimately through a lot of tests to study, just a few simple; extract wind speed for the neural network, we use 39 sets of data into training and testing The two parts of the obtained result of wind speed site wind data compare. I believe that if we intensify efforts to open sea trials, collecting a wealth of experimental data will be able to come to a very satisfactory result. In short, the authors developed by our team of ocean dynamic environment X-band radar monitoring system (XWCMS) \requirements of the research objectives, for further more in-depth study of the X-band radar to extract sea surface wind field and laid a solid foundation, and has important research value.
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