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A Class of Deterministic Sensing Matrices and Their Application in Harmonic Detection
[article]
2015
arXiv
pre-print
In this paper, a class of deterministic sensing matrices are constructed by selecting rows from Fourier matrices. These matrices have better performance in sparse recovery than random partial Fourier matrices. The coherence and restricted isometry property of these matrices are given to evaluate their capacity as compressive sensing matrices. In general, compressed sensing requires random sampling in data acquisition, which is difficult to implement in hardware. By using these sensing matrices
arXiv:1509.02628v1
fatcat:qmj7yskumzevthbi72y7b2orsq