改进的最佳子集降维STAP方法

针对空时最优处理器存在计算复杂度高、训练样本不足和杂波非均匀的问题，提出一种改进的降维方法。首先，利用空间谱相关系数表征目标和杂波轨迹的分离特性，将天线脉冲对的选择转化为最小化空间谱相关系数问题，降低了计算复杂度和杂波非均匀的影响；其次，对杂波噪声协方差矩阵进行特征值分解，结合互谱法选择特征向量构成降维矩阵，降低了对训练样本的需求；最后，仿真分析验证了所提方法的有效性。 

An Improved Optimal Subset Reduced-rank Method

An improved reduced-rank approach was presented to cope with the defections of high computational complexity, lack of training samples and clutter heterogeneity, existing in space-time optimum processor. Firstly, spatial spectral correlation coefficient was employed to depict the separation between object and clutter trajectory, and thus the selection of antenna-pulse pairs were converted to the minimization of the spatial spectral correlation coefficient. This step reduced the computational complexity and impression of clutter heterogeneity. Secondly, eigenvalue decomposition was implemented on the clutter-plus-noise covariance matrix, and eigenvectors were selected based on cross spectral to consist reduced-rank matrix. This step reduced the need for training samples. Finally, simulations prove the validity of proposed method.