使用极化知识的非均匀杂波数据选择器

A data selector in nonhomogeneous clutter using polarization knowledge

摘要:

为了解决雷达自适应处理中由于杂波的非均匀所带来的性能恶化问题,提出了一种基于极化特征知识的非均匀复合高斯杂波训练数据选择方法．该方法对训练数据单元进行极化散射矩阵的最大似然估计,然后计算每个估计值与先验极化知识的误差,以此作为区分野值的特征,从非均匀杂波中去除野值得到均匀的训练数据．仿真结果表明,该方法在野值杂波比较低时仍能够有效地去除野值,改善了杂波协方差矩阵的估计性能．

Abstract:

To solve the problem of performance degradation due to nonhomogeneous clutter in adaptive radar processing, a new training data selector in non-homogeneous compound-Gaussian clutter based on polarization knowledge was proposed. The polarization scatting matrix of every training sample was estimated using maximum likelihood estimation (MLE) method, and then the error between the estimation and prior polarization knowledge was used to remove outliers from training data. The performance of the knowledge-based algorithm was analyzed on simulated radar data. The results show that the new data selector removes outliers effectively when outlier-clutter ratio is low and achieves a satisfactory performance level for the estimation of clutter covariance matrix.