Abstract
| Original language | English |
|---|---|
| Title of host publication | IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014 |
| Publisher | IEEE |
| Publication date | May 2014 |
| Pages | 1035-1039 |
| ISBN (Print) | 978-1-4799-2892-7 |
| DOIs | |
| Publication status | Published - May 2014 |
| Event | IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) - Firenze, Italy Duration: 4 May 2014 → 9 May 2014 Conference number: 18874 |
Conference
| Conference | IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) |
|---|---|
| Number | 18874 |
| Country | Italy |
| City | Firenze |
| Period | 04/05/2014 → 09/05/2014 |
| Series | I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings |
|---|---|
| ISSN | 1520-6149 |
Cite this
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Joint Sparsity and Frequency Estimation for Spectral Compressive Sensing. / Nielsen, Jesper Kjær; Christensen, Mads Græsbøll; Jensen, Søren Holdt.
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014. IEEE, 2014. p. 1035-1039 (I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings).Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review
TY - GEN
T1 - Joint Sparsity and Frequency Estimation for Spectral Compressive Sensing
AU - Nielsen, Jesper Kjær
AU - Christensen, Mads Græsbøll
AU - Jensen, Søren Holdt
PY - 2014/5
Y1 - 2014/5
N2 - Parameter estimation from compressively sensed signals has recently received some attention. We here also consider this problem in the context of frequency sparse signals which are encountered in many application. Existing methods perform the estimation using finite dictionaries or incorporate various interpolation techniques to estimate the continuous frequency parameters. In this paper, we show that solving the problem in a probabilistic framework instead produces an asymptotically efficient estimator which outperforms existing methods in terms of estimation accuracy while still having a low computational complexity. Moreover, the proposed algorithm is also able to make inference about the sparsity level of the measured signal. The simulation code is available online.
AB - Parameter estimation from compressively sensed signals has recently received some attention. We here also consider this problem in the context of frequency sparse signals which are encountered in many application. Existing methods perform the estimation using finite dictionaries or incorporate various interpolation techniques to estimate the continuous frequency parameters. In this paper, we show that solving the problem in a probabilistic framework instead produces an asymptotically efficient estimator which outperforms existing methods in terms of estimation accuracy while still having a low computational complexity. Moreover, the proposed algorithm is also able to make inference about the sparsity level of the measured signal. The simulation code is available online.
U2 - 10.1109/ICASSP.2014.6853754
DO - 10.1109/ICASSP.2014.6853754
M3 - Article in proceeding
SN - 978-1-4799-2892-7
T3 - I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
SP - 1035
EP - 1039
BT - IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
PB - IEEE
ER -