PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Gencaga D., Ertuzun A., Kuruoglu E. E. Modeling of non-stationary autoregressive alpha-stable processes by particle filters. In: Digital Signal Processing, vol. 18 (3) pp. 465 - 478. Elsevier, 2008.
 
 
Abstract
(English)
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.
URL: http://scienceserver.cilea.it/cgi-bin/sciserv.pl?collection=journals&journal=10512004
DOI: 10.1016/j.dsp.2007.04.011
Subject Alpha-stable distributions
Non-stationary processes
Particle filtering
Sequential Monte Carlo
Bayesian estimation
Impulsive processes
Skewed processes
G.3 Probabilistic algorithms (including Monte Carlo)
G.3 Probability and Statistics. Stochastic processes
60G52 Stable processes
60E07 Infinitely divisible distributions; stable distributions
65C05 Monte Carlo methods


Icona documento 1) Download Document PDF


Icona documento Open access Icona documento Restricted Icona documento Private

 


Per ulteriori informazioni, contattare: Librarian http://puma.isti.cnr.it

Valid HTML 4.0 Transitional