Estimating period from sparse, noisy timing data

Barry G. Quinn, I. Vaughan L Clarkson, Robby G. McKilliam

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

    Abstract

    The problem discussed in this paper is that of estimating the period of a sequence of periodic events when the measurements of the occurrence times are noisy and sparse. The problem is common to many signal processing applications, such as baud estimation from zero-crossings in telecommunications and in pulse repetition interval estimation in electronic support measures. Previous algorithms have been based on periodogram maximisation [1, 2], Euclidean algorithms [3-5], least-squares line search [6], lattice line search [7] and Gaussian maximum likelihood [8]. Until now, very little has been known about the asymptotic statistical properties of any such algorithm. In this paper, a new algorithm is proposed, based on a modified least-squares approach. Under very general properties, the estimators of the system parameters are shown to have excellent (theoretical) asymptotic statistical properties. These properties are illustrated using a number of simulations.

    LanguageEnglish
    Title of host publication2012 IEEE Statistical Signal Processing Workshop, SSP 2012
    Place of PublicationPiscataway, N.J.
    PublisherInstitute of Electrical and Electronics Engineers (IEEE)
    Pages193-196
    Number of pages4
    ISBN (Print)9781467301831
    DOIs
    Publication statusPublished - 2012
    Event2012 IEEE Statistical Signal Processing Workshop, SSP 2012 - Ann Arbor, MI, United States
    Duration: 5 Aug 20128 Aug 2012

    Other

    Other2012 IEEE Statistical Signal Processing Workshop, SSP 2012
    CountryUnited States
    CityAnn Arbor, MI
    Period5/08/128/08/12

    Fingerprint

    Maximum likelihood
    Telecommunication
    Signal processing

    Cite this

    Quinn, B. G., Clarkson, I. V. L., & McKilliam, R. G. (2012). Estimating period from sparse, noisy timing data. In 2012 IEEE Statistical Signal Processing Workshop, SSP 2012 (pp. 193-196). [6319657] Piscataway, N.J.: Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/SSP.2012.6319657
    Quinn, Barry G. ; Clarkson, I. Vaughan L ; McKilliam, Robby G. / Estimating period from sparse, noisy timing data. 2012 IEEE Statistical Signal Processing Workshop, SSP 2012. Piscataway, N.J. : Institute of Electrical and Electronics Engineers (IEEE), 2012. pp. 193-196
    @inproceedings{0d3a90745081464e9fc87e8735fb2c5e,
    title = "Estimating period from sparse, noisy timing data",
    abstract = "The problem discussed in this paper is that of estimating the period of a sequence of periodic events when the measurements of the occurrence times are noisy and sparse. The problem is common to many signal processing applications, such as baud estimation from zero-crossings in telecommunications and in pulse repetition interval estimation in electronic support measures. Previous algorithms have been based on periodogram maximisation [1, 2], Euclidean algorithms [3-5], least-squares line search [6], lattice line search [7] and Gaussian maximum likelihood [8]. Until now, very little has been known about the asymptotic statistical properties of any such algorithm. In this paper, a new algorithm is proposed, based on a modified least-squares approach. Under very general properties, the estimators of the system parameters are shown to have excellent (theoretical) asymptotic statistical properties. These properties are illustrated using a number of simulations.",
    author = "Quinn, {Barry G.} and Clarkson, {I. Vaughan L} and McKilliam, {Robby G.}",
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    Quinn, BG, Clarkson, IVL & McKilliam, RG 2012, Estimating period from sparse, noisy timing data. in 2012 IEEE Statistical Signal Processing Workshop, SSP 2012., 6319657, Institute of Electrical and Electronics Engineers (IEEE), Piscataway, N.J., pp. 193-196, 2012 IEEE Statistical Signal Processing Workshop, SSP 2012, Ann Arbor, MI, United States, 5/08/12. https://doi.org/10.1109/SSP.2012.6319657

    Estimating period from sparse, noisy timing data. / Quinn, Barry G.; Clarkson, I. Vaughan L; McKilliam, Robby G.

    2012 IEEE Statistical Signal Processing Workshop, SSP 2012. Piscataway, N.J. : Institute of Electrical and Electronics Engineers (IEEE), 2012. p. 193-196 6319657.

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

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    Quinn BG, Clarkson IVL, McKilliam RG. Estimating period from sparse, noisy timing data. In 2012 IEEE Statistical Signal Processing Workshop, SSP 2012. Piscataway, N.J.: Institute of Electrical and Electronics Engineers (IEEE). 2012. p. 193-196. 6319657 https://doi.org/10.1109/SSP.2012.6319657