A Robust Autofocus Algorithm for ISAR Imaging of Moving Targets

摘要

A robust autofocus approach, referred to as AUTOCLEAN (AUTOfocus via CLEAN), is proposed for the motion compensation in ISAR (inverse synthetic aperture radar) imaging of moving targets. It is ap arameteric algorithm based on a very flexible data model which takes into account arbitrary range migration and arbitrary phase errors across the synthetic aperture than may be induced by unwanted radial motion of the target as well as propagation or system instability. AUTOCLEAN can be classified as a multiple scatterer algorithm (MSA), but it differs considerably from other existing MSAs in several aspects: a) dominant scatterers are selected automatically in the two-dimesional (2-D) image domain; B) scatterers may not be well-isolated or very dominant; C) phase and RCS (radar cross section) information from each selected scatterer are combined in an optimal way; d) the troublesome phase unwrapping step is avoided. AUTOCLEAN is computationally efficient and involves only a sequence of FFTs (fast Fourier transforms). Another good feature associated with AUTOCLEAN is that its performance can be progressively improved by assuming a larger number of dominat scatterers for the target. Hence it can be easily configured for real-time applications including, for example, ATR (automatic target recognition) of non-cooperative moving targets, and for some other applications where the image quality is of the major concern but not the computational time including, for example, for the development and maintenance of low observable aircrafts. Numerical and experimental results have shown that AUTOCLEAN is a very robust autofocus tool for ISAR imaging.