机载雷达三维空时自适应相关域降维算法

由于利用了俯仰维的自适应能力，三维空时自适应处理(three-dimensional space-time adaptive processing, 3D-STAP)能够获得比传统二维空时自适应处理(2D-STAP)更好的性能，但同时在运算量和采样数目的要求都将急剧增大。为了克服这个问题，提出了一种基于相关域的机载雷达三维空时自适应降维算法，即利用空时相关矩阵的子矩阵，将最优空时处理的二次代价函数转化为两个二次代价函数，并迭代求解这两个二次代价函数的两个低维权向量，所提算法能够明显降低计算复杂度和样本数目要求。基于仿真和实测数据的实验验证了算法的有效性。

Dimension-reduced 3D-STAP for airborne radars based on correlation matrix

Because of employing elevation adaptivity,three-dimensional space-time adaptive processing(3D-STAP) can achieve a better performance than conventional two-dimensional space-time adaptive processing(2D) STAP at the cost of higher computational load and sample support requirement.To overcome this shortcoming,a dimension-reduced 3D-STAP method for airborne radars based on correlation matrix is developed.In the proposed method,the quadratic cost function used in the optimum STAP is converted into two quadratic ...