A weighted eigenvector autofocus method for sparse-aperture ISAR imaging

Abstract

With the development of multi-functional radar systems, inverse synthetic aperture radar (ISAR) imaging with sparse-aperture (SA) data has drawn considerable attention in the recent years. Motion compensation and imaging are among the most significant challenges that SA-ISAR imaging frequently faces. In this paper, we focus on the autofocus scheme, in which a modified eigenvector-based autofocus method is proposed. In the method, different weights are endued to different range cells according to their signal-to-noise ratios (SNRs). Using the weights, the contribution from the range cells with high SNR is enhanced, yielding accuracy improvement in phase error estimation. What is more is that to improve the estimation precision, an iterative scheme is introduced. Experimental results show that the proposal is not only robust to severe noise but also applicable to ISAR imaging with different SA patterns. Detailed comparisons are given in order to show the superiorities of the proposal in phase adjustment for ISAR data.

Sparsity-Driven Synthetic Aperture Radar Imaging: Reconstruction, autofocusing, moving targets, and compressed sensing

“…In [31], phase error estimation is performed by comparing and aligning sparsity-driven images produced from a sequence of smaller coherent processing intervals, for which motion errors can be assumed to be tolerable. For sparse aperture ISAR imaging, [32] proposes first to remove the phase errors by a weighted eigenvector-based phase correction method and then to form the image by sparsity-driven imaging. The study in [33] demonstrates the effects of phase errors on sparsity-driven imaging and presents results obtained by implementing PGA on sparsity-driven reconstructions.…”