A Study on Wind Turbine Clutter Suppression Based on Morphological Component Separation

As the time-varying wind turbine clutter (WTC) seriously affects the detection performance of weather radar, the WTC suppression based on morphological component separation is studied. Firstly, the static ground clutter is filtered to decrease the morphological components of the radar echo. Then the morphological component analysis is implemented and the overlapping rate of window in short-time Fourier transform is improved to reduce the spectrum leakage of WTC. Finally, the basis pursuit and split augmented Lagrangian shrinkage algorithm are presented to decompose radar return signal into the sum of the weather signal and the WTC sparsely. Simulation results show that the proposed algorithm can effectively improve the mitigation performance of WTC in low signal-to-noise ratio environments.