Ionospheric imaging with compressed sensing

Compressed sensing is a novel theory of sampling and reconstruction that has emerged in the past several years. It seeks to leverage the inherent sparsity of natural images to reduce the number of necessary measurements to a sub-Nyquist level. We discuss how ideas from compressed sensing can benefit ionospheric imaging in two ways. Compressed sensing suggests signal reconstruction techniques that take advantage of sparsity, offering us new ways of interpreting data, especially for undersampled problems. One example is radar imaging. We explain how compressed sensing can be used for radar imaging and show results that suggest improved performance over existing techniques. In addition to benefitting the way we use data, compressed sensing can improve how we gather data, allowing us to shift complexity from sensing to reconstruction. One example is airglow imaging, wherein we propose replacing CCD-based imagers with single-pixel, compressive imagers. This will reduce the cost of airglow imagers and allow access to spatial information at infrared wavelengths. We show preliminary simulation results suggesting this technique may be feasible for airglow imaging.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2013

Author dc:creator

• Harding, Brian

Contributors dc:contributor

• Makela, Jonathan J.

• ionospheric imaging
• radar imaging
• compressed sensing
• inverse methods
• ionospheric irregularities
• compressive imaging

Statement dc:rights

• Copyright 2013 Brian Harding

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/44090

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/44090