In this paper, we propose a new estimator of the covariance matrix parameters when observations follow a mixture of a deterministic Compound-Gaussian (CG) and a white Gaussian noise. In particular, the covariance matrix of the CG contribution is assumed to be expressed as the Kronecker product of two low-rank matrices, which is a structure often involved in MIMO array processing. The proposed estimator is then obtained by maximizing the likelihood of the data with the use of a specifically tailored block Majorization-Minimization (MM) algorithm. Finally, the method is evaluated in terms of adaptive filtering on a MIMO-STAP radar setting, showing important improvements over standard processing.

在本文中，当观测遵循确定性复合高斯（CG）和白高斯噪声的混合时，我们提出了协方差矩阵参数的新估计器。特别地，假定CG贡献的协方差矩阵表示为两个低秩矩阵的Kronecker乘积，这是MIMO阵列处理中经常涉及的结构。然后，使用专门定制的块主化-最小化（MM）算法，通过使数据的可能性最大来获得拟议的估计器。最后，根据MIMO-STAP雷达设置上的自适应滤波对方法进行了评估，显示出对标准处理的重要改进。