Journal article
Block Iterative Reweighted Algorithms for Super-Resolution of Spectrally Sparse Signals
IEEE signal processing letters, Vol.22(12), pp.2319-2313
07/30/2015
DOI: 10.1109/LSP.2015.2478854
Abstract
We propose novel algorithms that enhance the performance of recovering
unknown continuous-valued frequencies from undersampled signals. Our iterative
reweighted frequency recovery algorithms employ the support knowledge gained
from earlier steps of our algorithms as block prior information to enhance
frequency recovery. Our methods improve the performance of the atomic norm
minimization which is a useful heuristic in recovering continuous-valued
frequency contents. Numerical results demonstrate that our block iterative
reweighted methods provide both better recovery performance and faster speed
than other known methods.
Details
- Title: Subtitle
- Block Iterative Reweighted Algorithms for Super-Resolution of Spectrally Sparse Signals
- Creators
- Myung Cho - University of IowaKumar Vijay Mishra - University of IowaJian-Feng CaiWeiyu Xu - University of Iowa
- Resource Type
- Journal article
- Publication Details
- IEEE signal processing letters, Vol.22(12), pp.2319-2313
- DOI
- 10.1109/LSP.2015.2478854
- ISSN
- 1070-9908
- eISSN
- 1558-2361
- Language
- English
- Date published
- 07/30/2015
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984197450302771
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