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

^*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

8 Citations (Scopus)

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.

Original language	English
Title of host publication	2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09
Pages	642-645
Number of pages	4
DOIs	https://doi.org/10.1109/SSP.2009.5278493
Publication status	Published - 2009
MoE publication type	A4 Conference publication
Event	IEEE Statistical Signal Processing Workshop - Cardiff, United Kingdom Duration: 31 Aug 2009 → 3 Sept 2009 Conference number: 15

Workshop

Workshop	IEEE Statistical Signal Processing Workshop
Abbreviated title	SSP
Country/Territory	United Kingdom
City	Cardiff
Period	31/08/2009 → 03/09/2009

Keywords

Basis pursuit
Compressed sensing
Nonstationary spectral estimation
Rihaczek spectrum
Wigner-Ville spectrum

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10.1109/SSP.2009.5278493

Cite this

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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",

}

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. p. 642-645 5278493.

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

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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

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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

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Workshop

Keywords

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