基于投影近似子空间跟踪技术的自聚焦算法

摘要

The eigenvector method for maximum-likelihood estimation of phase error has better algorithmic performance than phase gradient autofocus (PGA). However, this method requires eigendecomposition of the sample covariance matrix, which is a computationally expensive task and also limits the real-time application. In order to overcome such difficulty, an autofocus algorithm using the projection approximation subspace tracking (PAST) approach is proposed. With this method, the procedures of covariance matrix estimation and eigendecomposition can be avoided and the computational cost can be reduced to the level of that of PGA. Monte Carlo tests and real SAR data validate that the new approach outperforms PGA.%基于特征向量法的自聚焦算法具有比相位梯度自聚焦(Phase Gradient Autofocus,简称PGA)算法更好的算法性能,但该算法必须对协方差矩阵进行特征分解,所以运算量大.利用投影近似子空间跟踪(Projection Approximation Subspace Tracking,简称PAST)技术的自聚焦算法可以解决上述问题.通过实际数据处理结果对比,证明基于PAST 技术的自聚焦算法是一种可满足实时处理要求的有效自聚焦方法.