Compressive nonstationary spectral estimation using parsimonious random sampling of the ambiguity function

Alexander Jung; Georg Tauböck; Franz Hlawatsch

doi:10.1109/SSP.2009.5278493

Compressive nonstationary spectral estimation using parsimonious random sampling of the ambiguity function

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

8 Sitaatiot (Scopus)

Abstrakti

We propose a compressive estimator for the discrete Rihaczek spectrum (RS) of a time-frequency sparse, underspread, nonstationary random process. The new estimator uses a compressed sensing technique to achieve a reduction of the number of measurements. The measurements are randomly located samples of the ambiguity function of the observed signal. We provide a bound on the mean-square estimation error and demonstrate the performance of the estimator by means of simulation results. The proposed RS estimator can also be used for estimating the Wigner-Ville spectrum (WVS) since for an underspread process the RS and WVS are almost equal.

Alkuperäiskieli	Englanti
Otsikko	2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09
Sivut	642-645
Sivumäärä	4
DOI - pysyväislinkit	https://doi.org/10.1109/SSP.2009.5278493
Tila	Julkaistu - 2009
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	IEEE Statistical Signal Processing Workshop - Cardiff, Iso-Britannia Kesto: 31 elok. 2009 → 3 syysk. 2009 Konferenssinumero: 15

Workshop

Workshop	IEEE Statistical Signal Processing Workshop
Lyhennettä	SSP
Maa/Alue	Iso-Britannia
Kaupunki	Cardiff
Ajanjakso	31/08/2009 → 03/09/2009

Pääsy asiakirjaan

10.1109/SSP.2009.5278493

Muut tiedostot ja linkit

Linkki julkaisuun Scopuksessa

Siteeraa tätä

@inproceedings{d00a1a1a10bc4d0b897af4362c397c3b,

title = "Compressive nonstationary spectral estimation using parsimonious random sampling of the ambiguity function",

abstract = "We propose a compressive estimator for the discrete Rihaczek spectrum (RS) of a time-frequency sparse, underspread, nonstationary random process. The new estimator uses a compressed sensing technique to achieve a reduction of the number of measurements. The measurements are randomly located samples of the ambiguity function of the observed signal. We provide a bound on the mean-square estimation error and demonstrate the performance of the estimator by means of simulation results. The proposed RS estimator can also be used for estimating the Wigner-Ville spectrum (WVS) since for an underspread process the RS and WVS are almost equal.",

keywords = "Basis pursuit, Compressed sensing, Nonstationary spectral estimation, Rihaczek spectrum, Wigner-Ville spectrum",

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

year = "2009",

doi = "10.1109/SSP.2009.5278493",

language = "English",

isbn = "9781424427109",

pages = "642--645",

booktitle = "2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09",

note = "IEEE Statistical Signal Processing Workshop, SSP ; Conference date: 31-08-2009 Through 03-09-2009",

}

Jung, A, Tauböck, G & Hlawatsch, F 2009, Compressive nonstationary spectral estimation using parsimonious random sampling of the ambiguity function. julkaisussa 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09., 5278493, Sivut 642-645, IEEE Statistical Signal Processing Workshop, Cardiff, Iso-Britannia, 31/08/2009. https://doi.org/10.1109/SSP.2009.5278493

Compressive nonstationary spectral estimation using parsimonious random sampling of the ambiguity function. / Jung, Alexander; Tauböck, Georg; Hlawatsch, Franz.
2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09. 2009. s. 642-645 5278493.

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

TY - GEN

T1 - Compressive nonstationary spectral estimation using parsimonious random sampling of the ambiguity function

AU - Jung, Alexander

AU - Tauböck, Georg

AU - Hlawatsch, Franz

N1 - Conference code: 15

PY - 2009

Y1 - 2009

N2 - We propose a compressive estimator for the discrete Rihaczek spectrum (RS) of a time-frequency sparse, underspread, nonstationary random process. The new estimator uses a compressed sensing technique to achieve a reduction of the number of measurements. The measurements are randomly located samples of the ambiguity function of the observed signal. We provide a bound on the mean-square estimation error and demonstrate the performance of the estimator by means of simulation results. The proposed RS estimator can also be used for estimating the Wigner-Ville spectrum (WVS) since for an underspread process the RS and WVS are almost equal.

AB - We propose a compressive estimator for the discrete Rihaczek spectrum (RS) of a time-frequency sparse, underspread, nonstationary random process. The new estimator uses a compressed sensing technique to achieve a reduction of the number of measurements. The measurements are randomly located samples of the ambiguity function of the observed signal. We provide a bound on the mean-square estimation error and demonstrate the performance of the estimator by means of simulation results. The proposed RS estimator can also be used for estimating the Wigner-Ville spectrum (WVS) since for an underspread process the RS and WVS are almost equal.

KW - Basis pursuit

KW - Compressed sensing

KW - Nonstationary spectral estimation

KW - Rihaczek spectrum

KW - Wigner-Ville spectrum

UR - http://www.scopus.com/inward/record.url?scp=72349094990&partnerID=8YFLogxK

U2 - 10.1109/SSP.2009.5278493

DO - 10.1109/SSP.2009.5278493

M3 - Conference article in proceedings

AN - SCOPUS:72349094990

SN - 9781424427109

SP - 642

EP - 645

BT - 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09

T2 - IEEE Statistical Signal Processing Workshop

Y2 - 31 August 2009 through 3 September 2009

ER -

Compressive nonstationary spectral estimation using parsimonious random sampling of the ambiguity function

Abstrakti

Workshop

Pääsy asiakirjaan

Muut tiedostot ja linkit

Sormenjälki

Siteeraa tätä