Two-dimensional wind field retrieval for wind Lidar based on relative total variation model
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摘要:
为解决传统速度-方位处理(Velocity-Azimuth Processing, VAP)算法在二维风场反演中存在的风速失真问题,引入相对总变分模型对其进行改进。基于风速失真与径向风速的相关性,构建阈值判据识别失真区域,并对该区域进行修正;采用相对总变分模型消除修正过程中引入的不规则纹理,提取二维风场的全局结构分量;进一步结合真实径向风速的纹理信息,恢复二维风场局部纹理特征。研究结果表明:改进后的算法能够有效地修正VAP算法存在的风速失真问题,与VAP算法初步反演结果相比,结合相对总变分模型的VAP算法的风速反演结果均方根误差降低了0.42 m / s,风向反演结果均方根误差降低了4.85°,有效提高了风场反演结果的准确性。
Abstract:
To address the issue of wind-speed distortion in the two-dimensional (2D) wind retrieval using traditional Velocity-Azimuth Processing (VAP) algorithm, the Relative Total Variation (RTV) model is incorporated to improve the algorithm. Based on the correlation between wind-speed distortion and radial wind speed, a threshold is established to identify distorted regions, enabling subsequent correction. Then the RTV model is employed to eliminate irregular textures generated during the correction process,and extract the 2D overall wind structure. Furthermore, the local texture features of 2D wind field are reconstructed by combining the texture information from the actual radial wind speed. Experimental results demonstrate that the improved algorithm can effectively mitigate the wind-speed distortion issue in VAP algorithm. Compared to the preliminary results retrieved by VAP algorithm, the improved algorithm reduces root mean square error by 0.42 m / s for wind speed and 4.85° for wind direction, significantly improving the accuracy of wind retrieval.