Name
Abstract | ||
|---|---|---|
In this paper, a new class of orthogonal circulant matrices built from deterministic sequences is proposed for convolution-based compressed sensing (CS). In contrast to random convolution, the coefficients of the underlying filter are given by the discrete Fourier transform of a deterministic sequence with good autocorrelation. Both uniform recovery and non-uniform recovery of sparse signals are investigated, based on the coherence parameter of the proposed sensing matrices. Many examples of the sequences are investigated, particularly the Frank-Zadoff-Chu (FZC) sequence, the $m$-sequence and the Golay sequence. A salient feature of the proposed sensing matrices is that they can not only handle sparse signals in the time domain, but also those in the frequency and/or or discrete-cosine transform (DCT) domain. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TSP.2012.2229994 | IEEE Transactions on Signal Processing |
Keywords | DocType | Volume |
compressed sensing,discrete Fourier transforms,discrete cosine transforms,filtering theory,DCT domain,FZC sequence,Frank-Zadoff-Chu sequence,Golay sequence,autocorrelation,convolutional compressed sensing,deterministic sequences,discrete-cosine transform,m-sequence,orthogonal circulant matrices,salient feature,sensing matrices,sparse signals,sparse signals recovery,time domain,Compressed sensing,Frank-Zadoff-Chu sequence,Golay sequence,nearly perfect sequences,random convolution | Journal | 61 |
Issue | ISSN | Citations |
3 | 1053-587X | 23 |
PageRank | References | Authors |
0.91 | 25 | 3 |