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

^*Tämän työn vastaava kirjoittaja

Ei julkaistu Aalto-yliopiston affiliaatiolla

Institute of Telecommunications

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

6 Sitaatiot (Scopus)

Abstrakti

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.

Alkuperäiskieli	Englanti
Otsikko	2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009
Sivut	3029-3032
Sivumäärä	4
DOI - pysyväislinkit	https://doi.org/10.1109/ICASSP.2009.4960262
Tila	Julkaistu - 2009
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	IEEE International Conference on Acoustics, Speech, and Signal Processing - Taipei, Taiwan Kesto: 19 huhtik. 2009 → 24 huhtik. 2009

Conference

Conference	IEEE International Conference on Acoustics, Speech, and Signal Processing
Lyhennettä	ICASSP
Maa/Alue	Taiwan
Kaupunki	Taipei
Ajanjakso	19/04/2009 → 24/04/2009

Pääsy asiakirjaan

10.1109/ICASSP.2009.4960262

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@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. julkaisussa 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009., 4960262, Sivut 3029-3032, IEEE International Conference on Acoustics, Speech, and Signal Processing, Taipei, Taiwan, 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. s. 3029-3032 4960262.

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

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 - https://www.scopus.com/pages/publications/70349214785

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

Abstrakti

Conference

Pääsy asiakirjaan

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Sormenjälki

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