机载雷达三维空时自适应相关域降维算法

摘要

Because of employing elevation adaptivity, threedimensional spacetime adaptive processing (3D-STAP) can achieve a better performance than conventional twodimensional 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 functions by using the submatrices of the space-time correlation matrix. By iteratively optimizing two lower dimensional weight vectors in two quadratic functions, the proposed method can significantly decrease the computational load and training samples requirement. Experiment results using both simulated data and measured radar data demonstrate the effectiveness of the proposed method.%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.