Subspace pursuit for compressive sensing signal reconstruction
IEEE Transactions on Information Theory
On compressive sensing applied to radar
Signal Processing
Average case analysis of multichannel sparse recovery using convex relaxation
IEEE Transactions on Information Theory
Spread spectrum for compressed sensing techniques in magnetic resonance imaging
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
Orthogonal Matching Pursuit for Sparse Signal Recovery With Noise
IEEE Transactions on Information Theory
Compressive MUSIC: Revisiting the Link Between Compressive Sensing and Array Signal Processing
IEEE Transactions on Information Theory
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Compressed sensing (CS) is applied to capture signals at sub-Nyquist rate when the sensing matrix satisfies the restricted isometry property (RIP). When in a dimension-restricted system which has small row dimension and not so good coherence, the RIP and measurement bound will not be satisfied, and compressed sensing can not be applied directly. In this letter, we propose the dimension spread CS to the dimension-restricted system by directed dimension spread and diversity dimension spread, which make the compressed sensing applicable. The spread dimension bounds for the proposed algorithms are deduced to guarantee exact recovery which are also proved by simulations. Meanwhile, the experimental comparisons for the directed dimension spread CS and diversity dimension spread CS are given and different CS recovery algorithms are carried out to show the effectiveness of the proposed algorithms in the dimension-restricted system. The diversity dimension spread CS outperforms the directed dimension spread CS for its effective dimension spread and diversity. The proposed algorithms can be directly applied in channel estimation and multiuser detection in overload system.