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Estimation of chirp signals by mcmc

  • Stony Brook University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

25 Scopus citations

Abstract

This paper considers the problem of parameter estimation of chirp signals by using the Bayesian methodology. The concept of "mirror points" for constant-Amplitude chirp signals is introduced, and its effect on the overall multicomponent chirp parameter estimation performance assessed. By combining the chirpogram with a Markov Chain Monte Carlo (MCMC) technique, it is shown that accurate est.imates can be obtained for signals comprising many chirps. Simulation results demonstrate that the parameter estimates are in agreement with the CRLB for SNR's as low as 2 dB.

Original languageEnglish
Title of host publicationSignal Processing Theory and Methods I
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages265-268
Number of pages4
ISBN (Electronic)0780362934
DOIs
StatePublished - 2000
Event25th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2000 - Istanbul, Turkey
Duration: Jun 5 2000Jun 9 2000

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume1
ISSN (Print)1520-6149

Conference

Conference25th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2000
Country/TerritoryTurkey
CityIstanbul
Period06/5/0006/9/00

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