Title :
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
fDate :
1/1/1999 12:00:00 AM
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;
Journal_Title :
Aerospace and Electronic Systems, IEEE Transactions on
