Modeling of non-stationary autoregressive alpha-stable processe
Shared by Deniz Gencaga, updated on Sep 22, 2010
Summary
- Author(s) :
- Deniz Gencaga, A. Ertuzun, E.E. Kuruoglu
- Abstract
In the literature, impulsive signals are mostly modeled by symmetric alpha-stable processes. To represent their temporal dependencies, usually autoregressive models with time-invariant coefficients are utilized. We propose a general sequential Bayesian modeling methodology where both unknown autoregressive coefficients and distribution parameters can be estimated successfully, even when they are time-varying. In contrast to most work in the literature on signal processing with alpha-stable distributions, our work is general and models also skewed alpha-stable processes. Successful performance of our method is demonstrated by computer simulations. We support our empirical results by providing posterior Cramer–Rao lower bounds. The proposed method is also tested on a practical application where seismic data events are modeled.
- Publication Name
- Digital Signal Processing
- Publication Location
- Vol. 18, Issue 3, pp. 465-478
- Year Published
- 2008
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