Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2015 22nd International Conference on Systems, Signals and Image Processing |
| Untertitel | proceedings of IWSSIP 2015 : 10-12 September 2015, London, UK |
| Herausgeber/-innen | Shahjahan Miah, Alena Uus, Panos Liatsis |
| Erscheinungsort | Piscataway, NJ |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 162-165 |
| Seitenumfang | 4 |
| ISBN (elektronisch) | 9781467383530 |
| ISBN (Print) | 9781467383523 |
| Publikationsstatus | Veröffentlicht - 2015 |
| Extern publiziert | Ja |
| Veranstaltung | 22nd International Conference on Systems, Signals and Image Processing, IWSSIP 2015 - London, Großbritannien / Vereinigtes Königreich Dauer: 10 Sept. 2015 → 12 Sept. 2015 |
Publikationsreihe
| Name | International Conference on Systems, Signals, and Image Processing |
|---|---|
| Herausgeber (Verlag) | IEEE |
| ISSN (Print) | 2157-8672 |
| ISSN (elektronisch) | 2157-8702 |
Abstract
A compressive sensing (CS) based approach is applied in conjunction with an adaptive basis re-weighting procedure for multi-dimensional stochastic process power spectrum estimation. In particular, the problem of sampling gaps in stochastic process records, occurring for reasons such as sensor failures, data corruption, and bandwidth limitations, is addressed. Specifically, due to the fact that many stochastic process records such as wind, sea wave and earthquake excitations can be represented with relative sparsity in the frequency domain, a CS framework can be applied for power spectrum estimation. By relying on signal sparsity, and the assumption that multiple records are available upon which to produce a spectral estimate, it has been shown that a re-weighted CS approach succeeds in estimating power spectra with satisfactory accuracy. Of key importance in this paper is the extension from one-dimensional vector processes to a broader class of problems involving multidimensional stochastic fields. Numerical examples demonstrate the effectiveness of the approach when records are subjected to up to 75% missing data.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Computernetzwerke und -kommunikation
- Informatik (insg.)
- Maschinelles Sehen und Mustererkennung
- Informatik (insg.)
- Signalverarbeitung
Zitieren
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- Harvard
- Apa
- Vancouver
- BibTex
- RIS
2015 22nd International Conference on Systems, Signals and Image Processing : proceedings of IWSSIP 2015 : 10-12 September 2015, London, UK. Hrsg. / Shahjahan Miah; Alena Uus; Panos Liatsis. Piscataway, NJ: Institute of Electrical and Electronics Engineers Inc., 2015. S. 162-165 ( International Conference on Systems, Signals, and Image Processing).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Compressive sensing for power spectrum estimation of multi-dimensional processes under missing data
AU - Zhang, Yuanjin
AU - Comerford, Liam
AU - Beer, Michael
AU - Kougioumtzoglou, Ioannis
PY - 2015
Y1 - 2015
N2 - A compressive sensing (CS) based approach is applied in conjunction with an adaptive basis re-weighting procedure for multi-dimensional stochastic process power spectrum estimation. In particular, the problem of sampling gaps in stochastic process records, occurring for reasons such as sensor failures, data corruption, and bandwidth limitations, is addressed. Specifically, due to the fact that many stochastic process records such as wind, sea wave and earthquake excitations can be represented with relative sparsity in the frequency domain, a CS framework can be applied for power spectrum estimation. By relying on signal sparsity, and the assumption that multiple records are available upon which to produce a spectral estimate, it has been shown that a re-weighted CS approach succeeds in estimating power spectra with satisfactory accuracy. Of key importance in this paper is the extension from one-dimensional vector processes to a broader class of problems involving multidimensional stochastic fields. Numerical examples demonstrate the effectiveness of the approach when records are subjected to up to 75% missing data.
AB - A compressive sensing (CS) based approach is applied in conjunction with an adaptive basis re-weighting procedure for multi-dimensional stochastic process power spectrum estimation. In particular, the problem of sampling gaps in stochastic process records, occurring for reasons such as sensor failures, data corruption, and bandwidth limitations, is addressed. Specifically, due to the fact that many stochastic process records such as wind, sea wave and earthquake excitations can be represented with relative sparsity in the frequency domain, a CS framework can be applied for power spectrum estimation. By relying on signal sparsity, and the assumption that multiple records are available upon which to produce a spectral estimate, it has been shown that a re-weighted CS approach succeeds in estimating power spectra with satisfactory accuracy. Of key importance in this paper is the extension from one-dimensional vector processes to a broader class of problems involving multidimensional stochastic fields. Numerical examples demonstrate the effectiveness of the approach when records are subjected to up to 75% missing data.
KW - Compressive Sensing
KW - Missing Data
KW - Power Spectrum
KW - Stochastic Process
UR - http://www.scopus.com/inward/record.url?scp=84961720991&partnerID=8YFLogxK
U2 - 10.1109/IWSSIP.2015.7314202
DO - 10.1109/IWSSIP.2015.7314202
M3 - Conference contribution
AN - SCOPUS:84961720991
SN - 9781467383523
T3 - International Conference on Systems, Signals, and Image Processing
SP - 162
EP - 165
BT - 2015 22nd International Conference on Systems, Signals and Image Processing
A2 - Miah, Shahjahan
A2 - Uus, Alena
A2 - Liatsis, Panos
PB - Institute of Electrical and Electronics Engineers Inc.
CY - Piscataway, NJ
T2 - 22nd International Conference on Systems, Signals and Image Processing, IWSSIP 2015
Y2 - 10 September 2015 through 12 September 2015
ER -