Multiple window time-frequency and time-scale analysis

Metin Bayram; Richard G. Baraniuk

doi:10.1117/12.255431

Multiple window time-frequency and time-scale analysis

Metin Bayram, Richard G. Baraniuk

Research output: Contribution to journal › Conference article › peer-review

3 Scopus citations

Abstract

We propose an extension of Thomson's multiple window spectrum estimation for stationary random processes to the time-varying spectrum estimation of non-stationary random processes. Unlike previous extensions of Thomson's method, in this paper we identify and utilize optimally concentrated window and wavelet functions for the timefrequency and time-scale planes respectively. Moreover, we develop a statistical test for detecting and extracting chirping line components.

Original language	English (US)
Pages (from-to)	174-185
Number of pages	12
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	2846
DOIs	https://doi.org/10.1117/12.255431
State	Published - Oct 22 1996
Event	Advanced Signal Processing Algorithms, Architectures, and Implementations VI 1996 - Denver, United States Duration: Aug 4 1996 → Aug 9 1996

Keywords

Multiple window method
Non-stationary processes
Spectrum estimation

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

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10.1117/12.255431

Cite this

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title = "Multiple window time-frequency and time-scale analysis",

abstract = "We propose an extension of Thomson's multiple window spectrum estimation for stationary random processes to the time-varying spectrum estimation of non-stationary random processes. Unlike previous extensions of Thomson's method, in this paper we identify and utilize optimally concentrated window and wavelet functions for the timefrequency and time-scale planes respectively. Moreover, we develop a statistical test for detecting and extracting chirping line components.",

keywords = "Multiple window method, Non-stationary processes, Spectrum estimation",

author = "Metin Bayram and Baraniuk, {Richard G.}",

note = "Funding Information: N00014—95—1—0849, and the Texas Advanced Technology Program, grant no. TX—ATP 003604—002. Funding Information: * This work was supported by the National Science Foundation, grant no. MIP—9457438, the Office of Naval Research, grant no. Publisher Copyright: {\textcopyright} 1996 SPIE. All rights reserved. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; Advanced Signal Processing Algorithms, Architectures, and Implementations VI 1996 ; Conference date: 04-08-1996 Through 09-08-1996",

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doi = "10.1117/12.255431",

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T1 - Multiple window time-frequency and time-scale analysis

AU - Bayram, Metin

AU - Baraniuk, Richard G.

N1 - Funding Information: N00014—95—1—0849, and the Texas Advanced Technology Program, grant no. TX—ATP 003604—002. Funding Information: * This work was supported by the National Science Foundation, grant no. MIP—9457438, the Office of Naval Research, grant no. Publisher Copyright: © 1996 SPIE. All rights reserved. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.

PY - 1996/10/22

Y1 - 1996/10/22

N2 - We propose an extension of Thomson's multiple window spectrum estimation for stationary random processes to the time-varying spectrum estimation of non-stationary random processes. Unlike previous extensions of Thomson's method, in this paper we identify and utilize optimally concentrated window and wavelet functions for the timefrequency and time-scale planes respectively. Moreover, we develop a statistical test for detecting and extracting chirping line components.

AB - We propose an extension of Thomson's multiple window spectrum estimation for stationary random processes to the time-varying spectrum estimation of non-stationary random processes. Unlike previous extensions of Thomson's method, in this paper we identify and utilize optimally concentrated window and wavelet functions for the timefrequency and time-scale planes respectively. Moreover, we develop a statistical test for detecting and extracting chirping line components.

KW - Multiple window method

KW - Non-stationary processes

KW - Spectrum estimation

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U2 - 10.1117/12.255431

DO - 10.1117/12.255431

M3 - Conference article

AN - SCOPUS:84923249186

VL - 2846

SP - 174

EP - 185

JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

SN - 0277-786X

T2 - Advanced Signal Processing Algorithms, Architectures, and Implementations VI 1996

Y2 - 4 August 1996 through 9 August 1996

ER -

Multiple window time-frequency and time-scale analysis

Abstract

Keywords

ASJC Scopus subject areas

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