PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Gencaga D., Kuruoglu E. E., Ertuzun A. Estimation of time-varying autoregressive symmetric alpha-stable processes using particle filters. In: European Signal Processing Conference (Antalya, Turkey, 4-8 September 2005). Proceedings, Suvisoft, 2005.
 
 
Abstract
(English)
In the last decade alpha-stable distributions have become a standard model for impulsive data. Especially the linear symmetric alpha-stable processes have found applications in various fields. When the process parameters are time-invariant, various techniques are available for estimation. However, time-invariance is an important restriction given that in many communications applications channels are time-varying. For such processes, we propose a relatively new technique, based on particle filters which obtained great success in tracking applications involving non-Gaussian signals and nonlinear systems. Since particle filtering is a sequential method, it enables us to track the time-varying autoregression coefficients of the alpha-stable processes. The method is tested both for abruptly and slowly changing autoregressive parameters of signals, where the driving noises are symmetric-alpha-stable processes and is observed to perform very well. Moreover, the method can easily be extended to skewed alpha-stable distributions.
Subject Alpha-stable distribution
Time varying autoregressive processes
Particle filtering
Bayesian estimation
G.3 Probability and statitistics. Probabilistic algorithms (including
G.3 Probability and statitistics. Stochastic processes


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