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Finite-Window RLS Algorithms

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posted on 22.02.2022, 03:24 by Lu ShenLu Shen, Yuriy Zakharov, Maciej Niedźwiecki, Artur Gańcza
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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Funding

EPSRC EP/P017975/1 and EP/R003297/1

UMO-2018/29/B/ST7/00325

History

Email Address of Submitting Author

lu.shen@york.ac.uk

ORCID of Submitting Author

0000-0001-8830-2238

Submitting Author's Institution

University of York

Submitting Author's Country

United Kingdom

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in Signal Processing

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