Wind-Direction Estimation from Single X-Band Marine Radar Image Improvement by Utilizing the DWT and Azimuth-Scale...

Current methods for wind-direction estimation from marine radar often struggle with occlusion areas and rain contamination. Prior research has shown that standard algorithms using single-curve fitting can yield inaccurate results when radar data are heavily blocked. This gap motivated the development of a new approach that addresses these limitations. Established knowledge includes the use of X-band radar for wind retrieval and the challenge of occlusion areas near ships. No prior work had resolved the issue of heavy data blockage in sailing ships. This paper introduces a novel method that improves accuracy in such conditions. The need for a more robust algorithm is clear, especially in real-world maritime settings. The study builds on existing radar imaging techniques but introduces a unique combination of DWT and azimuth-scale expansion. This approach aims to refine wind-direction estimation in complex marine environments.

The study aimed to develop a more accurate method for wind-direction estimation from X-band marine radar images. The specific problem addressed is the inaccuracy caused by occlusion areas and rain contamination in existing algorithms. The motivation comes from the limitations of single-curve fitting in heavily blocked radar data. The researchers propose using DWT and azimuth-scale expansion to improve results. The goal was to reduce root-mean-square error in wind-direction retrieval. The method was designed to function well even when radar data are heavily blocked. This approach targets sailing ships where standard algorithms fail. The study sought to provide a more reliable solution for real-world maritime applications.

The algorithm distinguishes rain-free and rain-contaminated images using occlusion zero-pixel percentage. It discards rain-contaminated images to improve data quality. The radar image is decomposed into low-frequency sub-images using 2D DWT. An appropriate low-frequency sub-image is selected for further analysis. The data near the ship bow are shifted to expand the azimuth scale. This step helps mitigate the influence of occlusion areas. A harmonic function is then least-square-fitted to the range-averaged radar return. The function is fitted as a function of the antenna look azimuth to determine wind direction. The method combines DWT with azimuth-scale expansion for improved accuracy. This approach is distinct from previous wind-direction retrieval algorithms.

The algorithm reduces the root-mean-square error by 7.84° compared to single-curve fitting. The results show improved wind-direction estimation in sailing ships. The method functions well even with heavily blocked radar data. The use of DWT and azimuth-scale expansion enhances accuracy. The harmonic function fitting provides a reliable wind-direction measurement. The algorithm performs better than previous approaches in real-world conditions. The reduction in error is statistically significant and practical. The method is more suitable for sailing ships than existing algorithms. The study demonstrates the effectiveness of combining DWT with azimuth-scale expansion. The results support the proposed method's superiority in complex maritime environments.

The study concludes that the proposed method improves wind-direction estimation from X-band marine radar images. The algorithm functions well even with heavily blocked data. The reduction in root-mean-square error supports the method's effectiveness. The use of DWT and azimuth-scale expansion is a novel approach. The results suggest that this method is more suitable for sailing ships. The algorithm's performance is better than single-curve fitting in real-world conditions. The study supports the use of this method in maritime applications. The findings align with the authors' stated goals of improving accuracy in wind-direction retrieval.