协方差矩阵结构的广义近似最大似然估计

针对相关复合高斯杂波背景下相邻杂波纹理分量可能相同的情况，将杂波均匀分组进行推广，结合归一化采样协方差矩阵估计，提出了广义杂波分组的归一化采样协方差矩阵估计方法(generalized normalized sample covariance matrix, GNSCM). 利用最大似然估计方法，进一步推导了广义杂波分组背景下协方差矩阵结构最大似然估计的迭代过程，以GNSCM 为初始化矩阵进行迭代，得到协方差矩阵结构的广义近似最大似然(generalized approximate maximum likelihood, GAML) 估计. GAML 是对现有方法近似最大似然(approximate maximum likelihood, AML) 估计和约束迭代杂波分组估计(constrained recursive clutter-clustered estimator, CRCCE) 的推广，具有更强的杂波适应能力. 仿真结果表明，针对非均匀分组杂波环境，与AML 估计和CRCCE 相比，GAML 具有更高的估计精度，且相应的自适应检测器具有更好的恒虚警率特性和检测性能.

Generalized Approximate Maximum Likelihood Estimation of Covariance Matrix Structure

By generalizing the clutter-clustered estimation method and considering the normalized sample covariance matrix (NSCM), a generalized NSCM(GNSCM) is proposed for covariance matrix structure estimation in correlated compound-Gaussian clutter. A maximum likelihood recursive estimation process of covariance matrix structure is derived in generalized clutter-clustered background. A generalized approximate maximum likelihood (GAML) estimator is then obtained by using GNSCM as the initialized estimation estimated matrix to recursive. GAML is an extension of the existing methods the approximate maximum likelihood (AML) and the constrained recursive clutter-clustered estimator (CRCCE). Simulation results show that, compared with
the two previous methods, GAML has higher estimation accuracy, and the corresponding adaptive detector has better constant false alarm ratio (CFAR) property and detection performance.