A MAP Approach for 1-Bit Compressive Sensing in Synthetic Aperture Radar Imaging

Author

Xiao Dong ; Yunhua Zhang

Author_Institution

Key Lab. of Microwave Remote Sensing, Center for Space Sci. & Appl. Res., Beijing, China

Volume

12

Issue

6

fYear

2015

fDate

Jun-15

Firstpage

1237

Lastpage

1241

Abstract

In this letter, we propose a compressive sensing approach for synthetic aperture radar (SAR) imaging of sparse scenes with 1-bit-quantized data. Within the framework of maximum a posteriori estimation, we formulate the SAR image reconstruction problem as a sparse optimization problem and then solve it using a first-order primal-dual algorithm. The processing results of both simulated and real radar data show that our approach can eliminate the ghost target caused by 1-bit quantization in high signal-to-noise ratio situations and suppress the noisy background very well.

Keywords

compressed sensing; image coding; image reconstruction; interference suppression; maximum likelihood estimation; natural scenes; optimisation; quantisation (signal); radar imaging; synthetic aperture radar; 1-bit compressive sensing; 1-bit quantization; MAP approach; SAR image reconstruction problem; first-order primal dual algorithm; maximum aposteriori estimation; noisy background suppression; radar data processing; signal-to-noise ratio; sparse optimization problem; sparse scenes; synthetic aperture radar imaging; Compressed sensing; Imaging; Quantization (signal); Radar imaging; Radar polarimetry; Synthetic aperture radar; 1-bit compressive sensing (CS); maximum a posteriori (MAP); sparsity; synthetic aperture radar (SAR);

fLanguage

English

Journal_Title

Geoscience and Remote Sensing Letters, IEEE

Publisher

ieee

ISSN

1545-598X

Type

jour

DOI

10.1109/LGRS.2015.2390623

Filename

7042303