Performance of Sparse Recovery Algorithms for the Reconstruction of Radar Images From Incomplete RCS Data

Author

Ji-Hoon Bae ; Byung-Soo Kang ; Kyung-Tae Kim ; Eunjung Yang

Author_Institution

Dept. of Electr. Eng., Pohang Univ. of Sci. & Technol., Pohang, South Korea

Volume

12

Issue

4

fYear

2015

fDate

Apr-15

Firstpage

860

Lastpage

864

Abstract

In this letter, we compare the performances of sparse recovery algorithms (SRAs) for the reconstruction of a 2-D inverse synthetic aperture radar (ISAR) image from incomplete radar-cross-section (RCS) data. The three methods considered for the SRA include the basis pursuit (BP), the BP denoising, and the orthogonal matching pursuit methods. The performances of the methods in terms of the reconstruction accuracy of the ISAR image are compared using the incomplete RCS data. In addition, traditional interpolation methods such as nearest-neighbor interpolation, linear interpolation, and spline interpolation are applied to the incomplete RCS data to reconstruct ISAR images, and their performances are compared to that of the SRAs.

Keywords

image denoising; image reconstruction; iterative methods; radar cross-sections; radar imaging; splines (mathematics); 2D ISAR image reconstruction; BP denoising; SRA; basis pursuit; incomplete RCS; inverse synthetic aperture radar; linear interpolation; nearest neighbor interpolation; orthogonal matching pursuit method; radar cross section; sparse recovery algorithms; spline interpolation; Accuracy; Image reconstruction; Interpolation; Matching pursuit algorithms; Radar cross-sections; Radar imaging; BP denoising (BPDN); Basis pursuit (BP); interpolation; inverse synthetic aperture radar (ISAR) image; orthogonal matching pursuit (OMP); radar; sparse recovery algorithm (SRA);

fLanguage

English

Journal_Title

Geoscience and Remote Sensing Letters, IEEE

Publisher

ieee

ISSN

1545-598X

Type

jour

DOI

10.1109/LGRS.2014.2364601

Filename

6948257