Efficient Superresolution SAR Imaging

Date of Award


Degree Name

M.S. in Electrical and Computer Engineering


Department of Electrical and Computer Engineering


Brian Rigling


While traditional Fourier methods of SAR imaging are well known in addition to being easy to implement, they have limitations in terms of quality, particularly with respect to speckle, scintillation, and side lobe artifacts. Methods of SAR imaging that have shown promise include superresolution methods like the Minimum Variance Method (MVM) and the Multiple Signal Classification (MUSIC) algorithm; however, these algorithms are computationally intense. Both algorithms require the estimation of a correlation matrix, and manipulations thereof, as well as computing the image spectrum through computation of a quadratic form for each image pixel. This thesis presents an efficient method for estimating the correlation matrix and shows how the structure of the correlation matrix can be exploited to efficiently compute the aforementioned superresolution methods.


Electrical Engineering, Synthetic Aperture Radar, Spectral Estimation, Signal Processing, Efficient Implementation, Superresolution Techniques

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