To address the problem that dynamic wind turbine clutter(WTC)significantly degrades the performance of weather radar,a WTC mitigation algorithm using morphological component analysis(MCA)with group sparsity is studied...To address the problem that dynamic wind turbine clutter(WTC)significantly degrades the performance of weather radar,a WTC mitigation algorithm using morphological component analysis(MCA)with group sparsity is studied in this paper.The ground clutter is suppressed firstly to reduce the morphological compositions of radar echo.After that,the MCA algorithm is applied and the window used in the short-time Fourier transform(STFT)is optimized to lessen the spectrum leakage of WTC.Finally,the group sparsity structure of WTC in the STFT domain can be utilized to decrease the degrees of freedom in the solution,thus contributing to better estimation performance of weather signals.The effectiveness and feasibility of the proposed method are demonstrated by numerical simulations.展开更多
文摘To address the problem that dynamic wind turbine clutter(WTC)significantly degrades the performance of weather radar,a WTC mitigation algorithm using morphological component analysis(MCA)with group sparsity is studied in this paper.The ground clutter is suppressed firstly to reduce the morphological compositions of radar echo.After that,the MCA algorithm is applied and the window used in the short-time Fourier transform(STFT)is optimized to lessen the spectrum leakage of WTC.Finally,the group sparsity structure of WTC in the STFT domain can be utilized to decrease the degrees of freedom in the solution,thus contributing to better estimation performance of weather signals.The effectiveness and feasibility of the proposed method are demonstrated by numerical simulations.
基金国家自然科学基金No.61701388+13 种基金教育部归国留学人员科研扶持项目No.K05055陕西省自然科学基础研究计划项目No.2016JM60792016年碑林区科技计划项目No.GX1605陕西省教育厅专项No.17JK0431The National Natural Science Foundation of China under Grant No.61701388the Scientific Research Foundation for the Returned Overseas Chinese ScholarsState Education Ministry of China under Grant No.K05055the Natural Science Basic Research Plan of Shaanxi Province under Grant No.2016JM6079the Science and Technology Project of Beilin District in 2016 under Grant No.GX1605the Special Item of Shaanxi Provincial Department of Education under Grant No.17JK0431
文摘壁画数字化修复工作极大降低了手工修复时带来的不可逆的风险。根据唐墓室壁画人工修复时先整体结构、后局部纹理的思路,提出一种基于形态学成分分析(morphological component analysis,MCA)分解的唐墓室壁画修复算法。首先结合唐墓室壁画的特点,采用改进的MCA方法进行图像分解,得到结构部分和纹理部分;然后根据图像分解后纹理和结构的复杂程度与稀疏程度,分别采用简化的全变分(total variation,TV)算法和K奇异值分解(K-singular value decomposition,K-SVD)算法进行修复。实验结果表明,该算法可兼顾纹理与结构的修复效果,唐墓室壁画中的裂缝现象的破损修复精度得到提高。
