COMPARISON OF RECONSTRUCTION ACCURACY OF SPARSE RECOVERY ALGORITHMS FOR GAPPED RCS DATA

Abstract

This study evaluates and compares the reconstruction accuracy of sparse recovery algorithms (SRAs) when we reconstruct a two-dimensional (2D) inverse synthetic aperture radar (ISAR) image from a gapped radar-cross-section (RCS) dataset. To analyze the influence of the gap on the reconstruction accuracy, we adopted a relative error (RE) in a 2D image domain, and evaluated the reconstruction accuracy of each method by calculating each RE value against the rate of missing data in the gap. The accuracy of the basis pursuit denoising method was better than that of other methods considered in this study, especially when we reconstructed the ISAR image from the gapped RCS dataset when signal-to-noise ratio was low. Consequently, the relaxed constraint rather than the strictly equality constraint for the SRA is more adequate for SRA applied to radar imaging. (C) 2015 Wiley Periodicals, Inc.