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- W1979857942 abstract "Compressed Sensing (CS) is an effective approach to reduce the required number of samples for reconstructing a sparse signal in an a priori basis, but may suffer severely from the issue of basis mismatch. In this paper we study the problem of simultaneously recovering multiple spectrally-sparse signals that are supported on the same frequencies lying arbitrarily on the unit circle. We propose an atomic norm minimization problem, which can be regarded as a continuous counterpart of the discrete CS formulation and be solved efficiently via semidefinite programming. Through numerical experiments, we show that the number of samples per signal may be further reduced by harnessing the joint sparsity pattern of multiple signals." @default.
- W1979857942 created "2016-06-24" @default.
- W1979857942 creator A5053809095 @default.
- W1979857942 date "2013-11-09" @default.
- W1979857942 modified "2023-09-26" @default.
- W1979857942 title "Joint Sparsity Recovery for Spectral Compressed Sensing" @default.
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- W1979857942 doi "https://doi.org/10.48550/arxiv.1311.2229" @default.
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