Articolo in rivista, 2008, ENG, 10.1016/j.dsp.2007.04.01
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
Alpha-stable distributions, Non-stationary processes, Particle filtering, Sequential Monte Carlo, Bayesian estimation, Impulsive processes, Skewed processes
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
CNR authors
External links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
URL: http://scienceserver.cilea.it/cgi-bin/sciserv.pl?collection=journals&journal=10512004
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:44184
DOI: 10.1016/j.dsp.2007.04.01
ISI Web of Science (WOS): 000256286800017