利用广义内积值迭代加权的空时协方差矩阵估计方法

精确估计协方差矩阵是空时自适应处理(STAP)的核心问题，基于最大似然的样本协方差矩阵估计方法仅适用于均匀检测环境。为了提高非均匀场景下协方差矩阵的估计精度，该文提出迭代加权的空时协方差矩阵估计方法。该方法依据广义内积值(GIP)与其统计均值的距离确定样本的加权系数，并通过建立广义内积直方图及迭代处理的方式进一步提高协方差矩阵的估计精度。仿真结果表明，该方法能够提高非均匀环境下协方差矩阵的估计性能。

Iterative Weighted Covariance Matrix Estimation Method for STAP Based on Generalized Inner Products

Accurate estimation of the clutter covariance matrix is the core issue of STAP. The sample covariance matrix estimation method based on maximum likelihood criterion is only applicable to homogeneous environment. For improving the estimation precision of covariance matrix under the heterogeneous environment, an iterative weighted covariance matrix estimation method is proposed. The proposed method determines the weighting factors of all samples according to the distance of Generalized Inner Product (GIP) value from the statistical average, and it improves the estimation precision further by establishing the probability histogram of GIP and iterative processing. The simulation results show that the proposed method can improve the performance of covariance matrix estimation under nonhomogeneous environment.