We address the covariance matrix estimation problem for radar adaptive detection in a non-Gaussian clutter environment. We first propose an estimation method based on α log-determinant divergence, which estimates the true covariance accurately by solving the geometric mean of the sample covariance matrix (SCM). Since the estimation performance would be seriously degraded when the number of secondary data is insufficient, a knowledge-aided method is then proposed. Under the similarity constraint between the a priori covariance and the true one, a closed form expression is derived by minimizing the α log-determinant divergence between the real covariance and the SCM. Simulation results verify the accuracy of the proposed algorithms in covariance estimation and superiority in target adaptive detection.

中文翻译：

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知识辅助协方差估计和雷达自适应检测

我们解决了在非高斯杂波环境下雷达自适应检测的协方差矩阵估计问题。我们首先提出一种基于对数行列式散度的估计方法，该方法通过求解样本协方差矩阵（SCM）的几何平均值来准确估算真实的协方差。由于当次要数据的数量不足时估计性能会严重下降，因此提出了一种知识辅助方法。在先验协方差和真实协方差之间的相似性约束下，通过最小化实际协方差与SCM之间的对数行列式方差来得出闭式表达式。仿真结果验证了该算法在协方差估计中的准确性以及目标自适应检测的优越性。