Conference proceeding
Precise semidefinite programming formulation of atomic norm minimization for recovering d-dimensional (D ≥ 2) off-the-grid frequencies
2014 Information Theory and Applications Workshop (ITA), pp.1-4
02/2014
DOI: 10.1109/ITA.2014.6804267
Abstract
Recent research in off-the-grid compressed sensing (CS) has demonstrated that, under certain conditions, one can successfully recover a spectrally sparse signal from a few time-domain samples even though the dictionary is continuous. In particular, atomic norm minimization was proposed in [1] to recover 1-dimensional spectrally sparse signal. However, in spite of existing research efforts [2], it was still an open problem how to formulate an equivalent positive semidefinite program for atomic norm minimization in recovering signals with d-dimensional (d ≥ 2) off-the-grid frequencies. In this paper, we settle this problem by proposing equivalent semidefinite programming formulations of atomic norm minimization to recover signals with d-dimensional (d ≥ 2) off-the-grid frequencies.
Details
- Title: Subtitle
- Precise semidefinite programming formulation of atomic norm minimization for recovering d-dimensional (D ≥ 2) off-the-grid frequencies
- Creators
- Weiyu Xu - University of IowaJian-Feng Cai - University of IowaKumar Vijay Mishra - University of IowaMyung Cho - University of IowaAnton Kruger - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- 2014 Information Theory and Applications Workshop (ITA), pp.1-4
- DOI
- 10.1109/ITA.2014.6804267
- Publisher
- IEEE
- Language
- English
- Date published
- 02/2014
- Academic Unit
- Electrical and Computer Engineering; Civil and Environmental Engineering
- Record Identifier
- 9984197174002771
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