Minimum redundancy space-time adaptive processing utilizing reconstructed covariance matrix
Minimum redundancy space-time adaptive processing utilizing reconstructed covariance matrix
复制标题
DOI：
10.1109/radar.2017.7944297
复制
发表时间：
2017-05
期刊：
影响因子：
--
通讯作者：
Ruiyang Li;Yikai Wang-;Zishu He;Jun Yu Li;Guohao Sun
中科院分区：
文献类型：
--
作者：
Ruiyang Li;Yikai Wang-;Zishu He;Jun Yu Li;Guohao Sun
最大空时自由度(DOF)受天线和脉冲数目的限制。基于最小冗余阵列的概念，提出了一种新的稀疏空时自适应处理(STAP)方案，通过安排阵列几何结构和时间采样间隔，使联合空时采样的位置满足最小冗余间隔。根据施加在杂波协方差矩阵上的结构信息，利用一系列矩阵基对原杂波协方差矩阵进行精确估计，在虚拟空间和时间元素可调的情况下，重构出高维的杂波协方差矩阵。仿真结果验证了所提公式的正确性，并显著提高了角度-多普勒分辨率。
The maximum space-time degrees of freedom (DOF) is restricted to the number of antennas and pulses. In this paper, a novel sparse space time adaptive processing (STAP) scheme is proposed based on the concept of minimum redundancy arrays.We arrange the array geometry and the temporal sampler interval to make the location of joint space-time samples satisfies minimum redundancy interval. According to the structural information imposed on clutter covariance matrix (CCM), the original CCM is estimated by a series of matrix basis accurately, and a higher dimension of CCM can be reconstructed while the virtual spatial and temporal elements are adjustable. Simulation results verify the proposed formulations and the angle-Doppler resolution is increased significantly.