Component statistical analysis of second order hidden periodicities

Authors:
I. Javorskyj;D. Dehay;I. Kravets
Affiliations:
Institute of Telecommunications, University of Technology and Life Sciences, Al. Prof. S. Kaliskiego, 7, 85796, Bydgoszcz, Poland;Institut de Recherche Mathématique de Rennes, CNRS UMR 6625, Université Rennes 2, Place H. Le Moal, 35043 Rennes cedex, France;Karpenko Physico-Mechanical Institute of the National Academy of Sciences of Ukraine, Naukova St. 5, 79601, Lviv, Ukraine
Venue:
Digital Signal Processing
Year:
2014

Citing 10
Cited 0

Statistical analysis of two nonlinear least-squares estimators of sine-wave parameters in the colored-noise case

Circuits, Systems, and Signal Processing
Coherent covariance analysis of periodically correlated random processes

Signal Processing
Periodically Correlated Random Sequences: Spectral Theory and Practice (Wiley Series in Probability and Statistics)

Periodically Correlated Random Sequences: Spectral Theory and Practice (Wiley Series in Probability and Statistics)
Component covariance analysis for periodically correlated random processes

Signal Processing
Linear filtration methods for statistical analysis of periodically correlated random processes-Part II: Harmonic series representation

Signal Processing
Linear filtration methods for statistical analysis of periodically correlated random processes-Part I: Coherent and component methods and their generalization

Signal Processing
Search-efficient methods of detection of cyclostationary signals

IEEE Transactions on Signal Processing
Cisoid parameter estimation in the colored noise case: asymptoticCramer-Rao bound, maximum likelihood, and nonlinear least-squares

IEEE Transactions on Signal Processing
Statistical tests for presence of cyclostationarity

IEEE Transactions on Signal Processing
Nonparametric time series analysis for periodically correlated processes

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

The component method is applied to define estimators of the periods for Gaussian periodically correlated random processes (mathematical model of stochastic oscillations). The properties of these period estimators are obtained using some small parameter method and the rate of convergence is shown to be optimal. Specific results for the simplest models of periodically correlated process are presented. Finally the method is illustrated with a simulated sequence and a real life vibration signal.