Director of Research (if dissertation) or Advisor (if thesis)
Makela, Jonathan J.
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
ionospheric imaging
radar imaging
compressed sensing
inverse methods
ionospheric irregularities
compressive imaging
Abstract
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.
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