Instantaneous frequency estimation using sequential Bayesian techniques

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

The online estimation of the instantaneous frequency (IF) of time-varying (TV) signals with highly nonlinear phase functions is a challenging problem. In this paper, we propose an IF estimation method using Bayesian techniques, which combines particle filtering and Markov Chain Monte Carlo (MCMC) methods, to sequentially estimate highly nonlinear TV frequency variations as piecewise linear functions. Simultaneously applying parameter estimation and model selection, the new technique is extended to the IF estimation of multicomponent signals. Using simulations, we demonstrate the performance of our approach for different signals and environments.

Original languageEnglish (US)
Title of host publicationConference Record - Asilomar Conference on Signals, Systems and Computers
Pages569-573
Number of pages5
DOIs
StatePublished - 2006
Event40th Asilomar Conference on Signals, Systems, and Computers, ACSSC '06 - Pacific Grove, CA, United States
Duration: Oct 29 2006Nov 1 2006

Other

Other40th Asilomar Conference on Signals, Systems, and Computers, ACSSC '06
CountryUnited States
CityPacific Grove, CA
Period10/29/0611/1/06

Fingerprint

Frequency estimation
Parameter estimation
Markov processes
Monte Carlo methods

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Li, Y., Papandreou-Suppappola, A., & Morrell, D. (2006). Instantaneous frequency estimation using sequential Bayesian techniques. In Conference Record - Asilomar Conference on Signals, Systems and Computers (pp. 569-573). [4176622] https://doi.org/10.1109/ACSSC.2006.354812

Instantaneous frequency estimation using sequential Bayesian techniques. / Li, Ying; Papandreou-Suppappola, Antonia; Morrell, Darryl.

Conference Record - Asilomar Conference on Signals, Systems and Computers. 2006. p. 569-573 4176622.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Li, Y, Papandreou-Suppappola, A & Morrell, D 2006, Instantaneous frequency estimation using sequential Bayesian techniques. in Conference Record - Asilomar Conference on Signals, Systems and Computers., 4176622, pp. 569-573, 40th Asilomar Conference on Signals, Systems, and Computers, ACSSC '06, Pacific Grove, CA, United States, 10/29/06. https://doi.org/10.1109/ACSSC.2006.354812
Li Y, Papandreou-Suppappola A, Morrell D. Instantaneous frequency estimation using sequential Bayesian techniques. In Conference Record - Asilomar Conference on Signals, Systems and Computers. 2006. p. 569-573. 4176622 https://doi.org/10.1109/ACSSC.2006.354812
Li, Ying ; Papandreou-Suppappola, Antonia ; Morrell, Darryl. / Instantaneous frequency estimation using sequential Bayesian techniques. Conference Record - Asilomar Conference on Signals, Systems and Computers. 2006. pp. 569-573
@inproceedings{8d2f6af33ee74b6fa8f969a284d01cdf,
title = "Instantaneous frequency estimation using sequential Bayesian techniques",
abstract = "The online estimation of the instantaneous frequency (IF) of time-varying (TV) signals with highly nonlinear phase functions is a challenging problem. In this paper, we propose an IF estimation method using Bayesian techniques, which combines particle filtering and Markov Chain Monte Carlo (MCMC) methods, to sequentially estimate highly nonlinear TV frequency variations as piecewise linear functions. Simultaneously applying parameter estimation and model selection, the new technique is extended to the IF estimation of multicomponent signals. Using simulations, we demonstrate the performance of our approach for different signals and environments.",
author = "Ying Li and Antonia Papandreou-Suppappola and Darryl Morrell",
year = "2006",
doi = "10.1109/ACSSC.2006.354812",
language = "English (US)",
isbn = "1424407850",
pages = "569--573",
booktitle = "Conference Record - Asilomar Conference on Signals, Systems and Computers",

}

TY - GEN

T1 - Instantaneous frequency estimation using sequential Bayesian techniques

AU - Li, Ying

AU - Papandreou-Suppappola, Antonia

AU - Morrell, Darryl

PY - 2006

Y1 - 2006

N2 - The online estimation of the instantaneous frequency (IF) of time-varying (TV) signals with highly nonlinear phase functions is a challenging problem. In this paper, we propose an IF estimation method using Bayesian techniques, which combines particle filtering and Markov Chain Monte Carlo (MCMC) methods, to sequentially estimate highly nonlinear TV frequency variations as piecewise linear functions. Simultaneously applying parameter estimation and model selection, the new technique is extended to the IF estimation of multicomponent signals. Using simulations, we demonstrate the performance of our approach for different signals and environments.

AB - The online estimation of the instantaneous frequency (IF) of time-varying (TV) signals with highly nonlinear phase functions is a challenging problem. In this paper, we propose an IF estimation method using Bayesian techniques, which combines particle filtering and Markov Chain Monte Carlo (MCMC) methods, to sequentially estimate highly nonlinear TV frequency variations as piecewise linear functions. Simultaneously applying parameter estimation and model selection, the new technique is extended to the IF estimation of multicomponent signals. Using simulations, we demonstrate the performance of our approach for different signals and environments.

UR - http://www.scopus.com/inward/record.url?scp=47049119233&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=47049119233&partnerID=8YFLogxK

U2 - 10.1109/ACSSC.2006.354812

DO - 10.1109/ACSSC.2006.354812

M3 - Conference contribution

AN - SCOPUS:47049119233

SN - 1424407850

SN - 9781424407859

SP - 569

EP - 573

BT - Conference Record - Asilomar Conference on Signals, Systems and Computers

ER -