Non-convex methods for spectrally sparse signal reconstruction via low-rank Hankel matrix completion
Spectrally sparse signals arise in many applications of signal processing. A spectrally sparse signal is a mixture of a few undamped or damped complex sinusoids. An important problem from practice is to reconstruct such a signal from partial time domain samples. Previous convex methods have the draw...
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| Format: | Others |
| Language: | English |
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University of Iowa
2018
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| Online Access: | https://ir.uiowa.edu/etd/6331 https://ir.uiowa.edu/cgi/viewcontent.cgi?article=7663&context=etd |