Name
Abstract | ||
|---|---|---|
In many applications of compressed sensing, the data acquisition involves convolution by a filter followed by subsampling. In this letter, we propose to construct a filter with real-valued coefficients by taking the discrete Fourier transform of a decimated binary Sidelnikov sequence. With a random subsampler, we prove that stable recovery can be guaranteed if a signal is sparse in the canonical or the FFT basis. Besides, simulation results also show that if a deterministic subsampler is used, the proposed system can offer similar reconstruction performance as that of a random Gaussian operator for a wide range of signal length. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/LSP.2014.2311659 | IEEE Signal Process. Lett. |
Keywords | Field | DocType |
random gaussian operator,signal sampling,real-valued coefficients,coherence,convolution,subsampling,deterministic subsampler,discrete fourier transform,fft,data acquisition,discrete fourier transforms,decimated binary sidelnikov sequence,convolutional compressed sensing,sidelnikov sequences,compressed sensing,gaussian processes,signal reconstruction,filter construction,fast fourier transforms,restricted isometry property,sparse matrices,vectors,simulation | Mathematical optimization,Pattern recognition,Convolution,Fast Fourier transform,Artificial intelligence,Gaussian process,Discrete Fourier transform,Signal reconstruction,Sparse matrix,Compressed sensing,Mathematics,Restricted isometry property | Journal |
Volume | Issue | ISSN |
21 | 5 | 1070-9908 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nam Yul Yu | 1 | 158 | 23.39 |
| Lu Gan | 2 | 324 | 25.46 |