Title
Approximate ML Estimation of the Period and Spectral Content of Multiharmonic Signals Without User Interaction
Abstract
The goal of this paper is to construct an approximate maximum-likelihood estimator to accurately estimate the period and spectral contents of a noisy periodic signal that has many frequency components. This is accomplished without user interaction. The signal data record has a total number of periods that is not necessarily an integer but is greater than four. Furthermore, the number of samples per period may not necessarily be an integer number. It is shown that the accuracy of the estimated results is superior to estimates that are devoid of variance weighting, such as those engendered by the least squares estimator.
Year
DOI
Venue
2012
10.1109/TIM.2012.2202163
Instrumentation and Measurement, IEEE Transactions
Keywords
Field
DocType
maximum likelihood estimation,signal processing,approximate ML estimation,approximate maximum-likelihood estimator,frequency component,multiharmonic signal,noisy periodic signal,signal data record,spectral content,Spectral analysis,estimation,least squares (LS) methods,maximum-likelihood (ML) estimation,uncertainty
Least squares,Integer,Signal processing,Periodic function,Weighting,Noise measurement,Maximum likelihood,Statistics,Mathematics,Estimator
Journal
Volume
Issue
ISSN
61
11
0018-9456
Citations 
PageRank 
References 
0
0.34
6
Authors
3
Name
Order
Citations
PageRank
Mikaya L. D. Lumori100.68
Johan Schoukens237658.12
John Lataire311117.70