Sparse Signal Recovery From Incomplete And Perturbed Data (2016)
Abstract / truncated to 115 words
Sparse signal recovery consists of algorithms that are able to recover undersampled high dimensional signals accurately. These algorithms require fewer measurements than traditional Shannon/Nyquist sampling theorem demands. Sparse signal recovery has found many applications including magnetic resonance imaging, electromagnetic inverse scattering, radar/sonar imaging, seismic data collection, sensor array processing and channel estimation. The focus of this thesis is on electromagentic inverse scattering problem and joint estimation of the frequency offset and the channel impulse response in OFDM. In the electromagnetic inverse scattering problem, the aim is to find the electromagnetic properties of unknown targets from measured scattered field. The reconstruction of closely placed point-like objects is investigated. The application of the greedy pursuit based sparse ... toggle 10 keywordssparse signal recovery – compressive sensing – inverse problems – matching pursuit algorithms – electromagnetic imaging – electromagnetic inverse scattering – carrier frequency offset – channel estimation – OFDM – block-sparsity
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