On the periodogram estimator of period from sparse, noisy timing data

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

doi:10.1109/ACSSC.2013.6810414

On the periodogram estimator of period from sparse, noisy timing data

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

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

4 Citations (Scopus)

Abstract

The problem discussed is that of estimating the period of a sequence of periodic events when the occurrence time measurements are noisy and sparse. The problem arises in signal processing applications such as baud estimation from zero-crossings in telecommunications and in pulse repetition interval estimation in electronic support measures. Estimation techniques have been based on periodogram maximisation [1][2], Euclidean algorithms [3][4][5], least squares line search [6], lattice line search [7], Gaussian maximum likelihood [8] and least squares [9]. Aside from [9], there has been no rigorous statistical analysis. In this paper, we show that the periodogram maximiser has excellent (theoretical) asymptotic statistical properties, illustrating them via simulation.

Original language	English
Title of host publication	Conference Record of the 47th Asilomar Conference on Signals, Systems and Computers
Editors	Michael B. Matthews
Place of Publication	Pistacaway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	879-883
Number of pages	5
ISBN (Electronic)	9781479923885, 9781479923908
ISBN (Print)	9781479923915
DOIs	https://doi.org/10.1109/ACSSC.2013.6810414
Publication status	Published - 2013
Event	2013 47th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States Duration: 3 Nov 2013 → 6 Nov 2013

Other

Other	2013 47th Asilomar Conference on Signals, Systems and Computers
Country/Territory	United States
City	Pacific Grove, CA
Period	3/11/13 → 6/11/13

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10.1109/ACSSC.2013.6810414

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title = "On the periodogram estimator of period from sparse, noisy timing data",

abstract = "The problem discussed is that of estimating the period of a sequence of periodic events when the occurrence time measurements are noisy and sparse. The problem arises in signal processing applications such as baud estimation from zero-crossings in telecommunications and in pulse repetition interval estimation in electronic support measures. Estimation techniques have been based on periodogram maximisation [1][2], Euclidean algorithms [3][4][5], least squares line search [6], lattice line search [7], Gaussian maximum likelihood [8] and least squares [9]. Aside from [9], there has been no rigorous statistical analysis. In this paper, we show that the periodogram maximiser has excellent (theoretical) asymptotic statistical properties, illustrating them via simulation.",

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doi = "10.1109/ACSSC.2013.6810414",

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booktitle = "Conference Record of the 47th Asilomar Conference on Signals, Systems and Computers",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

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Quinn, BG, Clarkson, IVL & McKilliam, R 2013, On the periodogram estimator of period from sparse, noisy timing data. in MB Matthews (ed.), Conference Record of the 47th Asilomar Conference on Signals, Systems and Computers., 6810414, Institute of Electrical and Electronics Engineers (IEEE), Pistacaway, NJ, pp. 879-883, 2013 47th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, United States, 3/11/13. https://doi.org/10.1109/ACSSC.2013.6810414

On the periodogram estimator of period from sparse, noisy timing data. / Quinn, Barry G.; Clarkson, I. Vaughan L; McKilliam, Robby.
Conference Record of the 47th Asilomar Conference on Signals, Systems and Computers. ed. / Michael B. Matthews. Pistacaway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2013. p. 879-883 6810414.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

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AU - Clarkson, I. Vaughan L

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N2 - The problem discussed is that of estimating the period of a sequence of periodic events when the occurrence time measurements are noisy and sparse. The problem arises in signal processing applications such as baud estimation from zero-crossings in telecommunications and in pulse repetition interval estimation in electronic support measures. Estimation techniques have been based on periodogram maximisation [1][2], Euclidean algorithms [3][4][5], least squares line search [6], lattice line search [7], Gaussian maximum likelihood [8] and least squares [9]. Aside from [9], there has been no rigorous statistical analysis. In this paper, we show that the periodogram maximiser has excellent (theoretical) asymptotic statistical properties, illustrating them via simulation.

AB - The problem discussed is that of estimating the period of a sequence of periodic events when the occurrence time measurements are noisy and sparse. The problem arises in signal processing applications such as baud estimation from zero-crossings in telecommunications and in pulse repetition interval estimation in electronic support measures. Estimation techniques have been based on periodogram maximisation [1][2], Euclidean algorithms [3][4][5], least squares line search [6], lattice line search [7], Gaussian maximum likelihood [8] and least squares [9]. Aside from [9], there has been no rigorous statistical analysis. In this paper, we show that the periodogram maximiser has excellent (theoretical) asymptotic statistical properties, illustrating them via simulation.

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T2 - 2013 47th Asilomar Conference on Signals, Systems and Computers

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ER -

On the periodogram estimator of period from sparse, noisy timing data

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