Synthetic aperture radar autofocus based on projection approximation subspace tracking

Abstract

An eigenvector method for maximum-likelihood estimation (MLE) of phase error has better algorithmic performance than phase gradient autofocus (PGA), which is implemented by the simultaneous processing of multiple-pulse vectors of the range-compressed data. 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, this study proposes a novel autofocus algorithm using the projection approximation subspace tracking (PAST) approach. With this methodology, the computational cost can be reduced effectively to the level of PGA via avoiding the procedures of covariance matrix estimation and eigendecomposition. Monte Carlo tests and real synthetic aperture radar (SAR) data validate that although undergoing performance loss compare with the original multiple-pulse MLE algorithm, the new approach outperforms the mostly used PGA.