Abstract
Abstract:
Two recursive least-square (RLS) adaptive filtering algorithms are most
often used in practice, the exponential and sliding (rectangular) window
RLS algorithms. This popularity is mainly due to existence of
low-complexity versions of these algorithms. However, these two windows
are not always the best choice for identification of fast time-varying
systems, when the identification performance is most important. In this
paper, we show how RLS algorithms with arbitrary finite-length windows
can be implemented at a complexity comparable to that of the exponential
and sliding window RLS algorithms. Then, as an example, we show an
improvement in the performance when using the proposed finite-window RLS
algorithm with the Hanning window for identification of fast
time-varying systems.
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