Maximum-likelihood period estimation from sparse, noisy timing data

McKilliam, R. G. and Clarkson, I.V.L. (2008). Maximum-likelihood period estimation from sparse, noisy timing data. In: A. H. Sayed, Proceedings of the International Conference on Acoustics Speech and Signal Processing. IEEE International Conference on Acoustics, Speech and Signal Processing, 2008. ICASSP 2008., Las Vegas, Nevada, U.S.A., (3697-3700). 30 March - 4 April 2008. doi:10.1109/ICASSP.2008.4518455


Author McKilliam, R. G.
Clarkson, I.V.L.
Title of paper Maximum-likelihood period estimation from sparse, noisy timing data
Conference name IEEE International Conference on Acoustics, Speech and Signal Processing, 2008. ICASSP 2008.
Conference location Las Vegas, Nevada, U.S.A.
Conference dates 30 March - 4 April 2008
Proceedings title Proceedings of the International Conference on Acoustics Speech and Signal Processing   Check publisher's open access policy
Journal name 2008 Ieee International Conference On Acoustics, Speech and Signal Processing, Vols 1-12   Check publisher's open access policy
Place of Publication Piscataway, New Jersey, USA
Publisher IEEE
Publication Year 2008
Sub-type Fully published paper
DOI 10.1109/ICASSP.2008.4518455
ISBN 978-1-4244-1483-3
ISSN 1520-6149
Editor A. H. Sayed
Start page 3697
End page 3700
Total pages 4
Collection year 2009
Language eng
Abstract/Summary The problem of estimating the period of a periodic point process is considered when the observations are sparse and noisy. There is a class of estimators that operate by maximizing an objective function over an interval of possible periods, notably the periodogram estimator of Fogel & Gavish and the line-search algorithms of Sidiropoulos et al. and Clarkson. For numerical calculation, the interval is sampled. However, it is not known how fine the sampling must be in order to ensure statistically accurate results. In this paper, a new estimator is proposed which eliminates the need for sampling. For the proposed statistical model, it calculates a maximum- likelihood estimate. It is shown that the expected arithmetic complexity of the algorithm is O(n3 log n) where n is the number of observations. Numerical simulations demonstrate the superior statistical performance of the new estimator.
Subjects E1
970101 Expanding Knowledge in the Mathematical Sciences
970109 Expanding Knowledge in Engineering
090609 Signal Processing
Keyword maximum likelihood estimation
Synchronization
Frequency hop communication
Q-Index Code E1
Q-Index Status Confirmed Code

 
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