A Comparative Study of Compressed Sensing Approaches for 3-D Synthetic Aperture Radar Image Reconstruction

Abstract

This paper investigates two compressed sensing (CS) approaches that can be used to reconstruct 3-D synthetic aperture radar (SAR) images with undersampled measurements. Combining CS with the range migration algorithm (RMA), using either Stolt transform or non-uniform fast Fourier transform (NUFFT), yields two different approaches: Stolt-CS and NUFFT-CS. These approaches can decrease the load of data acquisition while recovering satisfactory 3-D SAR images through l1-norm optimization. A simulated image is used as the ground truth to facilitate the comparative study. The 2-D structured similarity (SSIM) index is extended to 3-D to assess the quality of the reconstructed images. Both the simulation and the experimental reconstruction results demonstrate that the Stolt-CS contributes little to image quality improvement or computational complexity reduction due to the inaccuracy of the Stolt transform. In contrast, the NUFFT-CS achieves a good tradeoff between the reconstruction quality and the computational costs. © 2014 Elsevier Inc.

Department(s)

Electrical and Computer Engineering

Comments

National Sleep Foundation, Grant None

Keywords and Phrases

3-D radar imaging; Compressed sensing (CS); Range migration algorithm (RMA); Synthetic aperture radar (SAR)

International Standard Serial Number (ISSN)

1051-2004

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Elsevier, All rights reserved.

Publication Date

01 Jan 2014

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