由于时变风力涡轮机杂波(WTC)严重影响气象雷达的探测性能,文中研究了基于形态成分分离的风电场杂波抑制算法。该算法首先滤除静止地杂波,以减少回波信号中的形态成分分量;然后,引入形态成分分析思想,并改进短时傅里叶变换窗函数重叠率以减少WTC 频谱泄露;最后,通过采用基追踪和分裂增广拉格朗日收缩算法,实现气象信号和WTC高精度稀疏分离。仿真实验结果表明,该算法有效提高了低信噪比条件下WTC 的抑制性能。

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.

TN959.4

国家自然科学基金资助项目(61771182);中央高校业务费资助项目(B210202076)