Fast sparse period estimation

R. G. McKilliam, I. V L Clarkson, B. G. Quinn

    Research output: Contribution to journalArticlepeer-review

    17 Citations (Scopus)


    The problem of estimating the period of a point process from observations that are both sparse and noisy is considered. By sparse it is meant that only a potentially small unknown subset of the process is observed. By noisy it is meant that the subset that is observed, is observed with error, or noise. Existing accurate algorithms for estimating the period require O(N2) operations where N is the number of observations. By quantizing the observations we produce an estimator that requires only O(N log N) operations by use of the chirp z-transform or the fast Fourier transform. The quantization has the adverse effect of decreasing the accuracy of the estimator. This is investigated by Monte-Carlo simulation. The simulations indicate that significant computational savings are possible with negligible loss in statistical accuracy.

    Original languageEnglish
    Article number6874540
    Pages (from-to)62-66
    Number of pages5
    JournalIEEE Signal Processing Letters
    Issue number1
    Publication statusPublished - Jan 2015


    • Fast Fourier Transform
    • period estimation


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