On the periodogram estimators of periods from interleaved sparse, noisy timing data

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

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

    3 Citations (Scopus)

    Abstract

    We examine the problem of estimating the periods of interleaved periodic point processes. We are particularly interested in the case where times of arrival (TOAs) are either measured with noise or not measured at all. This can arise in communications surveillance, where communications signals of different bauds may lie within the same surveillance bandwidth, and likewise in Electronic Surveillance (ES), where pulses or scans from different radars are observed together. In [1], the authors developed a general asymptotic theory for the Bartlett point-process periodogram estimator of the period of a single periodic process. In this paper, we extend the model to multiple periodic processes, each with a distinct period. The TOAs are observed unlabelled and in time order, i.e., they are interleaved. The largest local maximizers of the periodogram are shown to be good estimators of the unknown periods, asymptotically, and central limit theorems are proved. Simulations highlight a number of practical problems, and some problems with outstanding solutions are suggested.

    Original languageEnglish
    Title of host publication2014 IEEE Workshop on Statistical Signal Processing, SSP 2014
    Place of PublicationPiscataway, NJ
    PublisherInstitute of Electrical and Electronics Engineers (IEEE)
    Pages232-235
    Number of pages4
    ISBN (Print)9781479949755
    DOIs
    Publication statusPublished - 2014
    Event2014 IEEE Workshop on Statistical Signal Processing, SSP 2014 - Gold Coast, QLD, Australia
    Duration: 29 Jun 20142 Jul 2014

    Other

    Other2014 IEEE Workshop on Statistical Signal Processing, SSP 2014
    CountryAustralia
    CityGold Coast, QLD
    Period29/06/142/07/14

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