Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings IEEE International Conference on Acoustics, Speech and Signal Processing. |
| Number of pages | 4 |
| Publisher | IEEE Press |
| Publication date | Mar 2012 |
| Pages | 3609-3612 |
| ISBN (Print) | 978-1-4673-0045-2 |
| ISBN (Electronic) | 978-1-4673-0044-5 |
| DOIs | |
| Publication status | Published - Mar 2012 |
| Event | 2012 IEEE International Conference on Acoustics, Speech and Signal Processing - Kyoto, Japan Duration: 25 Mar 2012 → 30 Mar 2012 |
Conference
| Conference | 2012 IEEE International Conference on Acoustics, Speech and Signal Processing |
|---|---|
| Country | Japan |
| City | Kyoto |
| Period | 25/03/2012 → 30/03/2012 |
| Series | I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings |
|---|---|
| ISSN | 1520-6149 |
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On Compressed Sensing and the Estimation of Continuous Parameters From Noisy Observations. / Nielsen, Jesper Kjær; Christensen, Mads Græsbøll; Jensen, Søren Holdt.
Proceedings IEEE International Conference on Acoustics, Speech and Signal Processing. . IEEE Press, 2012. p. 3609-3612 (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 - On Compressed Sensing and the Estimation of Continuous Parameters From Noisy Observations
AU - Nielsen, Jesper Kjær
AU - Christensen, Mads Græsbøll
AU - Jensen, Søren Holdt
PY - 2012/3
Y1 - 2012/3
N2 - Compressed sensing (CS) has in recent years become a very popular way of sampling sparse signals. This sparsity is measured with respect to some known dictionary consisting of a finite number of atoms. Most models for real world signals, however, are parametrised by continuous parameters corresponding to a dictionary with an infinite number of atoms. Examples of such parameters are the temporal and spatial frequency. In this paper, we analyse how CS affects the estimation performance of any unbiased estimator when we assume such infinite dictionaries. We base our analysis on the Cramer-Rao lower bound (CRLB) which is frequently used for benchmarking the estimation accuracy of unbiased estimators. For the popular sensing matrices such as the Gaussian sensing matrix, our analysis shows that compressed sensing on average degrades the estimation accuracy by at least the down-sample factor.
AB - Compressed sensing (CS) has in recent years become a very popular way of sampling sparse signals. This sparsity is measured with respect to some known dictionary consisting of a finite number of atoms. Most models for real world signals, however, are parametrised by continuous parameters corresponding to a dictionary with an infinite number of atoms. Examples of such parameters are the temporal and spatial frequency. In this paper, we analyse how CS affects the estimation performance of any unbiased estimator when we assume such infinite dictionaries. We base our analysis on the Cramer-Rao lower bound (CRLB) which is frequently used for benchmarking the estimation accuracy of unbiased estimators. For the popular sensing matrices such as the Gaussian sensing matrix, our analysis shows that compressed sensing on average degrades the estimation accuracy by at least the down-sample factor.
UR - http://www.scopus.com/inward/record.url?scp=84867612879&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2012.6288697
DO - 10.1109/ICASSP.2012.6288697
M3 - Article in proceeding
SN - 978-1-4673-0045-2
T3 - I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
SP - 3609
EP - 3612
BT - Proceedings IEEE International Conference on Acoustics, Speech and Signal Processing.
PB - IEEE Press
ER -