Estimating period from sparse, noisy timing data

Quinn, Barry G., Clarkson, I.Vaughan L. and McKilliam, Robby G. (2012). Estimating period from sparse, noisy timing data. In: 2012 IEEE Statistical Signal Processing Workshop, SSP 2012. IEEE Statistical Signal Processing Workshop, SSP 2012, Ann Arbour, MI United States, (193-196). 5 - 8 August 2012. doi:10.1109/SSP.2012.6319657


Author Quinn, Barry G.
Clarkson, I.Vaughan L.
McKilliam, Robby G.
Title of paper Estimating period from sparse, noisy timing data
Conference name IEEE Statistical Signal Processing Workshop, SSP 2012
Conference location Ann Arbour, MI United States
Conference dates 5 - 8 August 2012
Proceedings title 2012 IEEE Statistical Signal Processing Workshop, SSP 2012
Place of Publication Red Hook, NY United States
Publisher Curran Associates, Inc.
Publication Year 2012
Year available 2012
Sub-type Fully published paper
DOI 10.1109/SSP.2012.6319657
Open Access Status
ISBN 9781467301831
9781467301824
1467301825
9781467301817
Start page 193
End page 196
Total pages 4
Collection year 2013
Language eng
Abstract/Summary 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.
Subjects 1711 Signal Processing
Keyword modified least squares
nearest lattice point problem
Period estimation
pulse repetition interval
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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