Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 9th International Conference on Structural Dynamics, EURODYN 2014 |
| Editors | A. Cunha, P. Ribeiro, E. Caetano, G. Muller |
| Pages | 2995-2999 |
| Number of pages | 5 |
| ISBN (electronic) | 9789727521654 |
| Publication status | Published - 2014 |
| Externally published | Yes |
| Event | 9th International Conference on Structural Dynamics, EURODYN 2014 - Porto, Portugal Duration: 30 Jun 2014 → 2 Jul 2014 |
Publication series
| Name | Proceedings of the International Conference on Structural Dynamic , EURODYN |
|---|---|
| Volume | 2014-January |
| ISSN (Print) | 2311-9020 |
Abstract
A compressive sensing (CS) based approach for power spectrum estimation of stationary stochastic processes subject to missing data is developed. In general, stochastic process records such as wind and wave excitations can often be represented with relative sparsity in the frequency domain. Relying on this attribute, a CS framework can be applied to reconstruct a signal that contains sampling gaps, possibly occurring for reasons such as sensor failures, data corruption, limited bandwidth/storage capacity, and power outages. Specifically, first a Fourier basis is selected for the recorded signal expansion. Next, an L1-norm minimization procedure is performed for obtaining the sparsest Fourier based representation of the signal. Finally, power spectrum estimates are derived from the determined expansion coefficients directly circumventing reconstruction of the signal in the time domain. The technique is shown to estimate successfully the essential features such as the dominant spectral peaks of the recorded processes' power spectra. Further, it appears to be efficient even in cases with 75% missing data demonstrating superior performance in comparison with alternative existing techniques. A significant advantage of the approach relates to the fact that it performs satisfactorily even in the presence of noise. Several numerical examples demonstrate the versatility and reliability of the approach.
Keywords
- Compressive sensing, Missing data, Power spectrum estimation, Stochastic processes
ASJC Scopus subject areas
- Engineering(all)
- Building and Construction
- Engineering(all)
- Architecture
- Engineering(all)
- Civil and Structural Engineering
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Proceedings of the 9th International Conference on Structural Dynamics, EURODYN 2014. ed. / A. Cunha; P. Ribeiro; E. Caetano; G. Muller. 2014. p. 2995-2999 (Proceedings of the International Conference on Structural Dynamic , EURODYN; Vol. 2014-January).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - A compressive sensing based approach for estimating stochastic process power spectra subject to missing data
AU - Comerford, Liam
AU - Kougioumtzoglou, Ioannis A.
AU - Beer, Michael
PY - 2014
Y1 - 2014
N2 - A compressive sensing (CS) based approach for power spectrum estimation of stationary stochastic processes subject to missing data is developed. In general, stochastic process records such as wind and wave excitations can often be represented with relative sparsity in the frequency domain. Relying on this attribute, a CS framework can be applied to reconstruct a signal that contains sampling gaps, possibly occurring for reasons such as sensor failures, data corruption, limited bandwidth/storage capacity, and power outages. Specifically, first a Fourier basis is selected for the recorded signal expansion. Next, an L1-norm minimization procedure is performed for obtaining the sparsest Fourier based representation of the signal. Finally, power spectrum estimates are derived from the determined expansion coefficients directly circumventing reconstruction of the signal in the time domain. The technique is shown to estimate successfully the essential features such as the dominant spectral peaks of the recorded processes' power spectra. Further, it appears to be efficient even in cases with 75% missing data demonstrating superior performance in comparison with alternative existing techniques. A significant advantage of the approach relates to the fact that it performs satisfactorily even in the presence of noise. Several numerical examples demonstrate the versatility and reliability of the approach.
AB - A compressive sensing (CS) based approach for power spectrum estimation of stationary stochastic processes subject to missing data is developed. In general, stochastic process records such as wind and wave excitations can often be represented with relative sparsity in the frequency domain. Relying on this attribute, a CS framework can be applied to reconstruct a signal that contains sampling gaps, possibly occurring for reasons such as sensor failures, data corruption, limited bandwidth/storage capacity, and power outages. Specifically, first a Fourier basis is selected for the recorded signal expansion. Next, an L1-norm minimization procedure is performed for obtaining the sparsest Fourier based representation of the signal. Finally, power spectrum estimates are derived from the determined expansion coefficients directly circumventing reconstruction of the signal in the time domain. The technique is shown to estimate successfully the essential features such as the dominant spectral peaks of the recorded processes' power spectra. Further, it appears to be efficient even in cases with 75% missing data demonstrating superior performance in comparison with alternative existing techniques. A significant advantage of the approach relates to the fact that it performs satisfactorily even in the presence of noise. Several numerical examples demonstrate the versatility and reliability of the approach.
KW - Compressive sensing
KW - Missing data
KW - Power spectrum estimation
KW - Stochastic processes
UR - http://www.scopus.com/inward/record.url?scp=84994476677&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84994476677
T3 - Proceedings of the International Conference on Structural Dynamic , EURODYN
SP - 2995
EP - 2999
BT - Proceedings of the 9th International Conference on Structural Dynamics, EURODYN 2014
A2 - Cunha, A.
A2 - Ribeiro, P.
A2 - Caetano, E.
A2 - Muller, G.
T2 - 9th International Conference on Structural Dynamics, EURODYN 2014
Y2 - 30 June 2014 through 2 July 2014
ER -