Super resolution SAR imaging via parametric spectral estimation methods

Author

Bi, Zhaoqiang ; Li, Jian ; Liu, Zheng-She

Author_Institution

Dept. of Electr. & Comput. Eng., Florida Univ., Gainesville, FL, USA

Volume

35

Issue

1

fYear

1999

fDate

1/1/1999 12:00:00 AM

Firstpage

267

Lastpage

281

Abstract

Super resolution synthetic aperture radar (SAR) image formation via sophisticated parametric spectral estimation algorithms is considered. Parametric spectral estimation methods are devised based on parametric data models and are used to estimate the model parameters. Since SAR images rather than model parameters are often used in SAR applications, we use the parameter estimates obtained with the parametric methods to simulate data matrices of large dimensions and then use the fast Fourier transform (FFT) methods on them to generate SAR images with super resolution. Experimental examples using the MSTAR and Environmental Research Institute of Michigan (ERIM) data illustrate that robust spectral estimation algorithms can generate SAR images of higher resolution than the conventional FFT methods and enhance the dominant target features

Keywords

adaptive estimation; adaptive filters; data models; fast Fourier transforms; feature extraction; image resolution; parameter estimation; radar computing; radar imaging; radar resolution; synthetic aperture radar; RELAX algorithm; data matrices; fast Fourier transform; feature extraction; large dimensions; parametric data models; parametric spectral estimation methods; relaxation-based algorithms; robust spectral estimation algorithms; superresolution SAR imaging; Bismuth; Data mining; Data models; Feature extraction; History; Image resolution; Parameter estimation; Radar polarimetry; Robustness; Synthetic aperture radar;

fLanguage

English

Journal_Title

Aerospace and Electronic Systems, IEEE Transactions on

Publisher

ieee

ISSN

0018-9251

Type

jour

DOI

10.1109/7.745697

Filename

745697