A Novel Clutter Covariance Matrix Estimation Method Based on Feature Subspace for Space-Based Early Warning Radar
A Novel Clutter Covariance Matrix Estimation Method Based on Feature Subspace for Space-Based Early Warning Radar
复制标题
一种基于特征子空间的天基预警雷达杂波协方差矩阵估计方法
DOI：
10.1109/jstars.2021.3123648
复制
发表时间：
2021
影响因子：
5.5
通讯作者：
王永良
中科院分区：
文献类型：
--
作者：
张天夫;王志浩;乔宁;张双喜;王永良
对被测单元（CUT）的杂波协方差矩阵进行准确估计是空时自适应处理（STAP）算法中关键的一步。天基预警雷达（SBE）信号具有独特的非平稳特性。
Accurate estimation of the clutter covariance matrix for the cell under test (CUT) is a committed step in the spatial-temporal adaptive processing (STAP) algorithm. The unique nonstationary characteristic of signal for space-based early warning radar (SBEWR) leads to the spatial variation of training sample and the insufficient number of optional independent identically distributed (i.i.d.) training samples, which brings difficulties to training sample selection and covariance matrix estimation. To improve the estimation accuracy of clutter covariance matrix and the performance of STAP for SBEWR in a heterogeneous environment, a novel training sample selection and clutter covariance matrix estimation method is proposed. The method based on clutter subspace reconstruction and spectrum correction technology can improve the estimation accuracy of clutter covariance matrix in the case of nonstationary signals and heterogeneous environments. The clutter covariance matrix estimated by the proposed method is similar to the clutter covariance matrix of the CUT, and the performance of STAP is improved. The experimental results confirm the performance of the proposed method.