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
The classical Fourier self deconvolution has the problem in selecting simulated function and filter function, so its application in resolving overlaped peaks is limited. In this paper, using coarser approximation obta...The classical Fourier self deconvolution has the problem in selecting simulated function and filter function, so its application in resolving overlaped peaks is limited. In this paper, using coarser approximation obtained after wavelet transform as line shape function, a new electroanalytical method, Fourier self deconvolution oscillographic chronopotentiometry is founded. Experimental results show that this method can not only improve the resolution power and sensitivity, but also make selection of parameters in the processing of Fourier self deconvolution easy and simple.展开更多
基金supported by the National Natural Science Foundation of China(61571088)the State High-Tech Development Plan(the 863 Program)(2015AA7031093B2015AA8098088B)
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.
文摘The classical Fourier self deconvolution has the problem in selecting simulated function and filter function, so its application in resolving overlaped peaks is limited. In this paper, using coarser approximation obtained after wavelet transform as line shape function, a new electroanalytical method, Fourier self deconvolution oscillographic chronopotentiometry is founded. Experimental results show that this method can not only improve the resolution power and sensitivity, but also make selection of parameters in the processing of Fourier self deconvolution easy and simple.
