Estimating parameters in noisy low frequency exponentially damped sinusoids and exponentials

Barry G. Quinn*

*Corresponding author for this work

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

    7 Citations (Scopus)

    Abstract

    There has been much recent interest in damped sinusoidal models, probably as a result of their relevance to magnetic resonance imaging. In [1], a model which allowed the sinusoid to decay to 0 was examined, and a Fourier coefficient estimation procedure was proposed. [2] noted that in order for any asymptotic theory to be available, the decay should not be allowed to complete, and examined the asymptotic behavior of a Fourier coefficient procedure based on this assumption, for which the asymptotic behavior ofnonlinear least squares estimators had already been derived in [3]. In this paper, we consider the problem of estimating the frequency and damping factor when the frequency is so low that only a finite number of periods appear in the data. Additionally, we consider a Fourier technique for estimating the damping factor in a noisy real exponential.

    Original languageEnglish
    Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
    Place of PublicationPiscataway, NJ
    PublisherInstitute of Electrical and Electronics Engineers (IEEE)
    Pages4298-4302
    Number of pages5
    Volume2016-May
    ISBN (Electronic)9781479999880
    DOIs
    Publication statusPublished - 18 May 2016
    Event41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China
    Duration: 20 Mar 201625 Mar 2016

    Other

    Other41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
    Country/TerritoryChina
    CityShanghai
    Period20/03/1625/03/16

    Fingerprint

    Dive into the research topics of 'Estimating parameters in noisy low frequency exponentially damped sinusoids and exponentials'. Together they form a unique fingerprint.

    Cite this