基于最大似然估计的特征向量分解自聚焦算法利用最大特征值对应的特征向量实现对相位误差的估计。该方法虽然具备精确和稳健的性能，但需要对协方差矩阵进行特征分解，导致实际数据在处理中运算量巨大，对内存要求也很高，难以在实时合成孔径雷达(SAR)成像处理中应用。该文提出一种基于加权最大范数的自聚焦方法，通过求解二范数最大化的优化函数对目标特征向量进行估计，避免了特征值的分解过程，有效提升了运算效率；利用信噪比加权的思想，对不同距离单元赋予不同的权值，增强了优质特显点样本对相位误差的估计贡献，有效改善了自聚焦精度。通过实测SAR和ISAR数据处理验证了算法的有效性。%The eigenvector method for maximum-likelihood estimation of phase error can obtain ideal performance of phase error estimation by using the eigenvector corresponding to its largest eigenvalue. Although the method is accurate and robust, it requires eigen-decomposition of the sample covariance matrix, which is computationally expensive and limits its real-time applications. In this paper, a Weighted Maximum Norm Method (WMNM) for phase error estimation is proposed. The eigenvector of the maximum eigenvalue can be obtained directly by solving the problem of maximizing L-2 norm, which avoids the eigen-decomposition of the sample covariance matrix and reduces the computational cost greatly. By adding different weights to each range bin, the contribution of the range cells with high SNR can be enhanced. Experimental results of the measured data by SAR and Inverse SAR (ISAR) verify the validity of the proposed algorithm.
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