部分均匀海杂波中分组加权的协方差矩阵估计

本文主要研究空间部分均匀海杂波背景下协方差矩阵的估计问题。海杂波的空间部分均匀性和假目标干扰的不可避免性导致利用传统算法来估计海杂波协方差矩阵时存在较大的估计误差。为了减小该估计误差，本文对海杂波的参考样本进行分组处理，利用纹理的最大后验估计值作为加权系数，提出了分组加权样本协方差矩阵估计算法。考虑到假目标干扰的存在，利用协方差矩阵之间的差异提出了一致性因子，以确定干扰所在的分组，并剔除干扰。实测数据的实验结果表明，在存在假目标干扰的空间部分均匀海杂波背景下，本文提出的分组加权协方差矩阵估计算法不仅能有效剔除假目标，而且优于不分组算法约3dB。 

A grouped weighted covariance matrix estimator in partially homogeneous sea clutter

A covariance matrix estimator in spatially partially homogeneous sea clutter is addressed in this paper. The partial homogeneity of sea clutter and the unavoidable false targets lead to a large error when the covariance matrix of sea clutter is estimated by the traditional estimators. In order to reduce the estimation error, the secondary data of sea clutter are grouped, the maximum posteriori estimation of texture is used as a weighted coefficient in each group, and a grouped weighted sample covariance matrix (GW-SCM) estimator is proposed. Due to the existing false targets, a consistent factor measuring the difference between covariance matrices is proposed. The group where interference lies in is identified by the proposed parameter, and the interference is eliminated at last. The experimental results of the real data show that the proposed grouped covariance matrix estimator not only eliminates false targets effectively, but also outperforms the estimator without grouping about 3dB in the partially homogeneous sea clutter.