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Simplified Wiener LMS Tracking with Automatic Tuning of the
Step-Size
Jonas Rutström
Licentiate Thesis, Signals and Systems,
Uppsala University, March 2005.
Thesis in pdf (10.2M)
- Abstract:
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This work considers tracking of time-varying parameters
and automatic tuning of the step-size for the Simplified Wiener LMS
algorithm (SWLMS). When tracking time-varying parameters in
applications where the rate of change of the time-varying parameters
and the noise level may change frequently, it is of interest to
adjust the adaptation gain, or the step-size, on-line. The reason
for this is that proper manual tuning of the step-size in these
cases often is very time consuming, or maybe even impossible.
The
purpose of this work has been to find a promising step-size updating
algorithm to be used in combination with the SWLMS algorithm in
order to create an almost self-tuning algorithm that can be used
only with little help from the system designer. Various step-size
candidates are evaluated and compared in different tracking
scenarios.
In addition to the comparison of the different step-size
algorithms, a small study concerning two other tracking issues is
also performed. The first issue deals with the potential performance
gain obtained by introducing individual step-size control of the
different time-varying parameters. The second issue concerns the use
of specific information, available to the designer, about the
time-varying parameters and the characteristics of signals passed
through the time-varying system, that maybe can be applied to
improve the overall tracking performance.
In the end, a small case study is performed. Here the most
promising algorithms are implemented in a realistic communication
scenario. It is shown that the proposed methods are widely superior
compared to the traditional constant gain algorithms.
- References:
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Conference paper in IEEE VTC 2002-Fall
on gain adaptation in WLMS tracking algorithms.
- Paper describing the
Wiener LMS adaptation algorithm.
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Main entry in list of publ.
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Research on parameter tracking and adaptive filtering
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