Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
Signal processing with alpha-stable distributions and applications
Signal processing with alpha-stable distributions and applications
Application of the Positive Alpha-Stable Distribution
SPWHOS '97 Proceedings of the 1997 IEEE Signal Processing Workshop on Higher-Order Statistics (SPW-HOS '97)
Sequential parameter estimation of time-varying non-Gaussian autoregressive processes
EURASIP Journal on Applied Signal Processing
Modeling of non-stationary autoregressive alpha-stable processes by particle filters
Digital Signal Processing
Posterior Cramer-Rao bounds for discrete-time nonlinear filtering
IEEE Transactions on Signal Processing
Estimation for regression with infinite variance errors
Mathematical and Computer Modelling: An International Journal
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In this work, we present a novel method for modeling time-varying autoregressive impulsive signals driven by symmetric alpha stable distributions. The proposed method can be interpreted as a two-stage Gibbs sampler composed of a particle filter, which is capable of estimating the unknown time-varying autoregressive coefficients, and a hybrid Monte Carlo method for estimating the unknown but constant distribution parameters of a symmetric alpha stable process. This method is an alternative to a recently published technique in which both the autoregressive coefficients and the distribution parameters are estimated jointly within a single sequential Monte Carlo framework-the single particle filter technique. The proposed method achieves lower error variances in estimating the distribution parameters compared with the single sequential Monte Carlo technique, and thus, successfully models symmetric impulsive signals.