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.