Compressive sensing for 3D microwave imaging systems

Resource Information

The work Compressive sensing for 3D microwave imaging systems represents a distinct intellectual or artistic creation found in University of Missouri-St. Louis Libraries. This resource is a combination of several types including: Work, Language Material, Books.

Label

Compressive sensing for 3D microwave imaging systems

Statement of responsibility

by Hamed Kajbaf

Title variation

• Compressive sensing for 3 dimensional microwave imaging systems
• Compressive sensing for three dimensional microwave imaging systems

Creator

• Kajbaf, Hamed

Subject

• Electronic bookss
• Electronic dissertations
• Microwave imaging
• Signal processing
• Sparse matrices
• Synthetic aperture radar
• Electronic books

Genre

• Electronic dissertations
• Electronic bookss
• Electronic books

Language

eng

Summary

"Compressed sensing (CS) image reconstruction techniques are developed and experimentally implemented for wideband microwave synthetic aperture radar (SAR) imaging systems with applications to nondestructive testing and evaluation. These techniques significantly reduce the number of spatial measurement points and, consequently, the acquisition time by sampling at a level lower than the Nyquist-Shannon rate. Benefiting from a reduced number of samples, this work successfully implemented two scanning procedures: the nonuniform raster and the optimum path. Three CS reconstruction approaches are also proposed for the wideband microwave SAR-based imaging systems. The first approach reconstructs a full-set of raw data from undersampled measurements via L1-norm optimization and consequently applies 3D forward SAR on the reconstructed raw data. The second proposed approach employs forward SAR and reverse SAR (R-SAR) transforms in each L1-norm optimization iteration reconstructing images directly. This dissertation proposes a simple, elegant truncation repair method to combat the truncation error which is a critical obstacle to the convergence of the CS iterative algorithm. The third proposed CS reconstruction algorithm is the adaptive basis selection (ABS) compressed sensing. Rather than a fixed sparsifying basis, the proposed ABS method adaptively selects the best basis from a set of bases in each iteration of the L1-norm optimization according to a proposed decision metric that is derived from the sparsity of the image and the coherence between the measurement and sparsifying matrices. The results of several experiments indicate that the proposed algorithms recover 2D and 3D SAR images with only 20% of the spatial points and reduce the acquisition time by up to 66% of that of conventional methods while maintaining or improving the quality of the SAR images"--Abstract, p. iv

Related

• Quantitative and qualitative comparison of SAR images from incomplete measurements using compressed sensing and nonuniform FFT
• Compressed sensing for SAR-based wideband 3D microwave imaging system using nonuniform FFT
• Improving efficiency of microwave wideband imaging using compressed sensing techniques
• MST thesis. Electrical Engineering (Ph.D., 2012)
• 3D image reconstruction from sparse measurement of wideband millimeter wave SAR experiments
• Adaptive basis selection compressed sensing

Cataloging source

UMR

Degree

Ph. D.

Dissertation year

2012.

Granting institution

Missouri University of Science and Technology

Illustrations

illustrations

Index

no index present

Literary form

non fiction

Nature of contents

• dictionaries
• theses

Target audience

specialized