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The real-time monitoring of sea surface wind and wave information are crucial to the safety, performance and efficiency of various weather-sensitive on- and offshore operations, such as oil & gas platform drilling, port operations and offshore wind farming. This project plans to propose an accurate and robust method to estimate sea surface wind and wave parameters (e.g., wind speed, wind direction, wave height, etc) using a type of sensor called X-band marine radar. Compared to other traditional sensors such as buoy, X-band marine radar is a “dry” sensor deployed above water, which is low on maintenance cost. Although various methods have been developed to wind and wave information using radar images generated by electromagnetic waves, the presence of rain will negatively affect the quality of the image, leading to low estimation accuracy. In order to solve this problem, this project aims to develop a novel method to mitigate the influence of rain on radar and further improve estimation accuracy based on machine learning techniques.
Engineering - computer / electrical
Professional, scientific and technical services
Memorial University of Newfoundland
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