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Istituto di Scienza e Tecnologie dell'Informazione     
Gencaga D., Kuruoglu E. E., Ertuzun A. Estimation of Time-Varying Autoregressive Symmetric Alpha Stable Processes by Particle Filters. The document has been submitted to Journal: IEEE Transactions on Signal Processing, Technical report, 2006.
 
 
Abstract
(English)
In this work, we propose a novel method to model time-varying autoregressive impulsive signals, which possess Symmetric Alpha Stable distributions. The proposed method is composed of a particle filter, which is capable of estimating the unknown, timevarying autoregressive coefficients and a Hybrid Monte Carlo method that is used for estimating the unknown statistical parameters of the Symmetric Alpha Stable Process. The performance of the proposed method is tested for different parameter values where the time variation of the autoregressive coefficients is taken to be as sinusoidal or random jumps. The successful performance of the developed method serves as a promising contribution in the modeling of impulsive signals, which are frequently seen in many areas, such as teletraffic in computer communications, radar and sonar applications and mobile communications.
Subject Alpha-stable distributions
Non-stationary processes
Time-varying autoregressive processes
Particle filtering
Sequential Monte Carlo
G.3 PROBABILITY AND STATISTICS . Probabilistic algorithms (includingMonte Carlo)
G.3 PROBABILITY AND STATISTICS . Stochastic processes
G.3 PROBABILITY AND STATISTICS . Time series analysis
60G52 Stable processes
60E07 Infinitely divisible distributions
stable distributions
62F15 Bayesian inference
65C05 Monte Carlo methods
82B80 Numerical methods (Monte Carlo, series resummation, etc.)


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