中文摘要：

利用参考样本估计海杂波的协方差矩阵，最典型的方法是归一化样本协方差矩阵（normalized sam—plecovariancematrix，NsCM），它隐含着假设参考样本的散斑分量具有相同的统计特性。这一假设成立与否取决于海杂渡是否具有空间均匀性。为了摆脱NSCM对散斑分量的假设，提出了一种基于对角加栽的协方差矩阵估计算法（NSCM—L），该算法自适应组合NSCM和单位矩阵，其组合系数随着参考样本的统计特性变化而自适应地变化。利用广义似然比检测器（generalized likelihood ratio detector，GLRT）检测实测海杂波中的扩展目标，实验结果表明，当分辨率为3m时，NSCM—L性能改善高达15dB。

英文摘要：

The most typical method to estimate the covariance matrix of sea clutter Dy seconaary bamples isthe normalized sample covariance matrix （NSCM）, which implies the speckle of secondary samples with the same statistical property. The hypothesis established or not depends on whether or not sea clutter is spatially homogeneous. In order to get rid of the hypothesis on speckles, a new covariance matrix estimation algorithm based on the diagonal loading （NSCM-L） is proposed. It combines NSCM with the unit matrix adaptively, where the coefficient of combination changes with the statistical property of secondary samples adaptively. Ex- perimental results show that, to detect the distributed targets in real sea clutter by a generalized likelihood ratio detector （GLRT）, the performance of NSCM-L improves as high as 15 dB in 3 m resolution.