SAR image processing using super resolution spectral estimation with SVD-periodogram method

This paper presents an SVD-periodogram method for synthetic aperture radar (SAR) imaging. The purpose of this work is to improve resolution and target separability of SAR images. An advantage of the SVD-periodogram method is noise robustness, reduction of sidelobes and resolution of spectral estimation. In this paper, it is demonstrated that the SVD-periodogram method shows better performance than the matched filtering method and the conventional super-resolution multiple signal classification (MUSIC) method in SAR image processing. The targets to be separated are modeled, and this modeled data is used to demonstrate the performance of algorithms.