Instantaneous frequency estimation using sequential Bayesian techniques

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

2 Scopus citations

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 of the 40th Asilomar Conference on Signals, Systems and Computers, ACSSC '06
Pages569-573
Number of pages5
DOIs
StatePublished - Dec 1 2006
Event40th Asilomar Conference on Signals, Systems, and Computers, ACSSC '06 - Pacific Grove, CA, United States
Duration: Oct 29 2006Nov 1 2006

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Other

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

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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    Li, Y., Papandreou-Suppappola, A., & Morrell, D. (2006). Instantaneous frequency estimation using sequential Bayesian techniques. In Conference Record of the 40th Asilomar Conference on Signals, Systems and Computers, ACSSC '06 (pp. 569-573). [4176622] (Conference Record - Asilomar Conference on Signals, Systems and Computers). https://doi.org/10.1109/ACSSC.2006.354812