Name
Abstract | ||
|---|---|---|
Tonal signals are shown as spectral peaks in the frequency domain. When the number of spectral peaks is small and the spectral signal is sparse, Compressive Sensing (CS) can be adopted to locate the peaks with a low-cost sensing system. In the CS scheme, a time domain signal is modelled as y = Phi F-1 s, where y and s are signal vectors in the time and frequency domains. In addition, F-1 and Phi are an inverse DFT matrix and a random-sampling matrix, respectively. For a given y and Phi, the CS method attempts to estimate s with l(0) or l(1) optimization. To generate the peak candidates, we adopt the frequency-domain information of , where is the extended version of y and (n) is zero when n is not elements of CS time instances. In this paper, we develop Gaussian statistics of. That is, the variance and the mean values of are examined. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1587/transfun.E98.A.1122 | IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES |
Keywords | Field | DocType |
compressive sensing, tonal signal detection, noise modeling | Noise floor,Detection theory,Speech recognition,Mathematics,Compressed sensing | Journal |
Volume | Issue | ISSN |
E98A | 5 | 0916-8508 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chenlin Hu | 1 | 0 | 0.34 |
| Jin Young Kim | 2 | 497 | 81.76 |
| Seung Ho Choi | 3 | 46 | 12.72 |
| Chang-Joo Kim | 4 | 153 | 20.04 |