CAJ | 학술논문

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。
정학고계협방차구진시공시자괄응처리(STAP)적핵심문제，기우최대사연적양본협방차구진고계방법부괄용우균균검측배경。위료제고비균균장경하협방차구진적고계정도，해문제출질대가권적공시협방차구진고계방법。해방법의거엄의내적치(GIP)여기통계균치적거리학정양본적가권계수，병통과건립엄의내적직방도급질대처리적방식진일보제고협방차구진적고계정도。방진결과표명，해방법능구제고비균균배경하협방차구진적고계성능。
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.