基于同伦稀疏STAP的低空风切变风速估计

在机载气象雷达前视阵下，由于杂波的距离依赖性导致独立同分布(independent identically distributed, IID)训练样本严重不足，传统的空时自适应处理(space-time adaptive processing, STAP)算法性能下降，风场速度估计不准。针对此问题，提出一种基于同伦稀疏STAP的低空风切变风速估计方法，该方法仅需少量IID训练样本就能够达到较好的风场速度估计效果。首先，利用同伦稀疏恢复算法估计杂波协方差矩阵，从而重构出高分辨率的杂波空时二维谱。然后，通过求解STAP处理器的最优权矢量自适应滤除杂波。最后，实现对风场风速的准确估计。仿真结果证明所提方法的有效性。

Wind speed estimation of low-altitude wind-shear based on homotopy sparse STAP

In airborne weather radar front view array, due to the distance dependence of clutter, the training samples of independent identically distributed (IID) are seriously insufficient. The performance of traditional space-time adaptive processing (STAP) algorithm is reduced, and wind field velocity estimation is inaccurate.To solve this problem, this paper proposes a low-altitude wind-shear wind speed estimation method based on homotopy sparse STAP. This method only needs a small number of IID training samples to achieve a better wind field velocity estimation effect.Firstly, the homotopy sparse recovery algorithm is used to estimate the clutter covariance matrix, and then the high resolution space-time spectrum of clutter is reconstructed. Then, the optimal weight vector of STAP processor is solved to filter the clutter adaptively. Finally, the wind speed is estimated accurately.Simulation results show the effectiveness of the proposed method.