Robust Wiener Design of Adaptation Laws with Constant Gains.
IFAC Workshop on Adaptation and Learning in Control and
Signal Processing (ALCOSP 2001), Como, Italy, August 29-31 2001.
© IFAC 2001
Conference proceedings published in: Sergio Bittanti, ed,
Adaptation and Learning in Control and Signal Processing 2001.
ISBN: 0 08 043683 8, Elsevier Publishing, Sept. 2002.
Filters can be introduced into LMS-like
adaptation algoritms to improve their
We here discuss the model-based design of
such filters when tracking coefficients of
linear regression models.
parameter variations are
modeled as ARIMA-processes which represent
prior information. The aim is to provide high
filtering, prediction or fixed lag smoothing
for arbitrary lags.
Since the second order properties of the
are in general not known exactly,
a robust design for a set of possible models
will be of interest.
We present a method
that minimizes the average tracking MSE,
based on probabilistic descriptions of the
The method is based on a novel signal transformation
that recasts the algorithm design into a
Wiener problem with uncertain parameter model,
which is to be solved iteratively.
The performance is illustrated
on the tracking of time-varying mobile radio
channels in ANSI-136 systems, based on a
model of the time-variations affected by
of the general constant-gain adaptation algorithms. (Complete report,
of stability and performance, for slow and fast variations.
The Wiener LMS
adaptation algorithm, a special case with low complexity.
A Case Study on IS-136 channels.
PhD Thesis by Lars Lindbom, 1995.
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