Articolo in rivista, 2008, ENG, 10.1016/j.dsp.2007.04.01

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

Gencaga D.; Ertuzun A.; Kuruoglu E. E.

Department of Electrical and Electronic Engineering, Bogazici University, Istanbul, Turkey; Department of Electrical and Electronic Engineering, Bogazici University, Istanbul, Turkey; CNR-ISTI, Pisa, Italy

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 odeling 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.

Digital signal processing (Print) 18 (3), pp. 465–478

Keywords

Alpha-stable distributions, Non-stationary processes, Particle filtering, Sequential Monte Carlo, Bayesian estimation, Impulsive processes, Skewed processes

CNR authors

Kuruoglu Ercan Engin

CNR institutes

ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"

ID: 44184

Year: 2008

Type: Articolo in rivista

Creation: 2009-07-29 00:00:00.000

Last update: 2017-12-20 08:20:27.000

External IDs

CNR OAI-PMH: oai:it.cnr:prodotti:44184

DOI: 10.1016/j.dsp.2007.04.01

ISI Web of Science (WOS): 000256286800017