Paper Info
Title
Continuous-Time Autoregressive Models Excited By Semi-Levy Process For Cyclostationary Signal Analysis
Abstract
In non-stationary signal modeling, it is critical to find an appropriate theoretical model with proper properties for analysing data. Models with cyclostationary (CS) property are prominent in signal processing analysis. In this paper, we extend the stationary Levy driven continuous-time autoregressive (CAR) model to the semi-Levy driven CAR (SL-CAR) model, which we show that has CS property. This model provides a platform for modeling non-stationary continuous-time processes. Using sample spectral coherence test, the CS behavior of simulated data from the SL-CAR(2) model is shown. Estimation of the parameters is followed by fitting VAR-GARCH process to the discretized state process and minimizing the distance between the covariance matrices of corresponding noise vectors. Numerical simulations provide evidence that the estimators are consistent, which is followed by computing the mean square errors. By using equally spaced sampling, the modes of empirical probability density functions of the estimators converge to the true values of the parameters when the sample size increases. Finally, we apply the SL-CAR(2) for modeling electrocardiogram biomedical signals and evaluate its performance. (C) 2021 Elsevier Inc. All rights reserved.
Year
DOI
Venue
2021
10.1016/j.dsp.2021.103195
DIGITAL SIGNAL PROCESSING
Keywords
DocType
Volume
Continuous-time autoregressive model, Cyclostationary signal, Semi-Levy process, VAR-GARCH process
Journal
118
ISSN
Citations 
PageRank 
1051-2004
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Mohammad Mohammadi100.34
Saeid Rezakhah200.68
Navideh Modarresi300.34
hamidreza amindavar421536.34