Compressive spectral estimation for nonstationary random processes

Alexander Jung; Georg Tauböck; Franz Hlawatsch

doi:10.1109/ICASSP.2009.4960262

Compressive spectral estimation for nonstationary random processes

Alexander Jung^*, Georg Tauböck, Franz Hlawatsch

^*Corresponding author for this work

Not published at Aalto University

Vienna University of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

6 Citations (Scopus)

Abstract

We propose a "compressive" estimator of the Wigner-Ville spectrum (WVS) for time-frequency sparse, underspread, nonstationary random processes. A novel WVS estimator involving the signal's Gabor coefficients on an undersampled time-frequency grid is combined with a compressed sensing transformation in order to reduce the number of measurements required. The performance of the compressive WVS estimator is analyzed via a bound on the mean square error and through simulations. We also propose an efficient implementation using a special construction of the measurement matrix.

Original language	English
Title of host publication	2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009
Pages	3029-3032
Number of pages	4
DOIs	https://doi.org/10.1109/ICASSP.2009.4960262
Publication status	Published - 2009
MoE publication type	A4 Conference publication
Event	IEEE International Conference on Acoustics, Speech, and Signal Processing - Taipei, Taiwan, Republic of China Duration: 19 Apr 2009 → 24 Apr 2009

Conference

Conference	IEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated title	ICASSP
Country/Territory	Taiwan, Republic of China
City	Taipei
Period	19/04/2009 → 24/04/2009

Keywords

Basis pursuit
Compressed sensing
Gabor expansion
Nonstationary spectral estimation
Sparse reconstruction
Wigner-Ville spectrum

Access to Document

10.1109/ICASSP.2009.4960262

Cite this

@inproceedings{88062edc1f6a48de99bab4842452a313,

title = "Compressive spectral estimation for nonstationary random processes",

abstract = "We propose a {"}compressive{"} estimator of the Wigner-Ville spectrum (WVS) for time-frequency sparse, underspread, nonstationary random processes. A novel WVS estimator involving the signal's Gabor coefficients on an undersampled time-frequency grid is combined with a compressed sensing transformation in order to reduce the number of measurements required. The performance of the compressive WVS estimator is analyzed via a bound on the mean square error and through simulations. We also propose an efficient implementation using a special construction of the measurement matrix.",

keywords = "Basis pursuit, Compressed sensing, Gabor expansion, Nonstationary spectral estimation, Sparse reconstruction, Wigner-Ville spectrum",

author = "Alexander Jung and Georg Taub{\"o}ck and Franz Hlawatsch",

year = "2009",

doi = "10.1109/ICASSP.2009.4960262",

language = "English",

isbn = "9781424423545",

pages = "3029--3032",

booktitle = "2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009",

note = "IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP ; Conference date: 19-04-2009 Through 24-04-2009",

}

Jung, A, Tauböck, G & Hlawatsch, F 2009, Compressive spectral estimation for nonstationary random processes. in 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009., 4960262, pp. 3029-3032, IEEE International Conference on Acoustics, Speech, and Signal Processing, Taipei, Taiwan, Republic of China, 19/04/2009. https://doi.org/10.1109/ICASSP.2009.4960262

Compressive spectral estimation for nonstationary random processes. / Jung, Alexander; Tauböck, Georg; Hlawatsch, Franz.
2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009. 2009. p. 3029-3032 4960262.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Compressive spectral estimation for nonstationary random processes

AU - Jung, Alexander

AU - Tauböck, Georg

AU - Hlawatsch, Franz

PY - 2009

Y1 - 2009

N2 - We propose a "compressive" estimator of the Wigner-Ville spectrum (WVS) for time-frequency sparse, underspread, nonstationary random processes. A novel WVS estimator involving the signal's Gabor coefficients on an undersampled time-frequency grid is combined with a compressed sensing transformation in order to reduce the number of measurements required. The performance of the compressive WVS estimator is analyzed via a bound on the mean square error and through simulations. We also propose an efficient implementation using a special construction of the measurement matrix.

AB - We propose a "compressive" estimator of the Wigner-Ville spectrum (WVS) for time-frequency sparse, underspread, nonstationary random processes. A novel WVS estimator involving the signal's Gabor coefficients on an undersampled time-frequency grid is combined with a compressed sensing transformation in order to reduce the number of measurements required. The performance of the compressive WVS estimator is analyzed via a bound on the mean square error and through simulations. We also propose an efficient implementation using a special construction of the measurement matrix.

KW - Basis pursuit

KW - Compressed sensing

KW - Gabor expansion

KW - Nonstationary spectral estimation

KW - Sparse reconstruction

KW - Wigner-Ville spectrum

UR - http://www.scopus.com/inward/record.url?scp=70349214785&partnerID=8YFLogxK

U2 - 10.1109/ICASSP.2009.4960262

DO - 10.1109/ICASSP.2009.4960262

M3 - Conference article in proceedings

AN - SCOPUS:70349214785

SN - 9781424423545

SP - 3029

EP - 3032

BT - 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009

T2 - IEEE International Conference on Acoustics, Speech, and Signal Processing

Y2 - 19 April 2009 through 24 April 2009

ER -

Compressive spectral estimation for nonstationary random processes

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this