Modeling of non-stationary autoregressive alpha-stable processes by particle filters

  • Authors:

  • Deniz Gençağa;Ayşın Ertüzün;Ercan E. Kuruoğlu

  • Affiliations:

  • Department of Electrical and Electronic Engineering, Boğaziçi University, 34342, Bebek, İstanbul, Turkey;Department of Electrical and Electronic Engineering, Boğaziçi University, 34342, Bebek, İstanbul, Turkey;ISTI, Area Della Ricerca CNR di Pisa, Via G. Moruzzi 1, 56124, Pisa, Italy

  • Venue:
  • Digital Signal Processing
  • Year:
  • 2008

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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.