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Abstract
Two recursive least-squares (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 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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Authors (4)
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Lu Shen
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Yuriy Zakharov
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Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.sigpro.2022.108599
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Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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SIGNAL PROCESSING
no. 198,
ISSN: 0165-1684 - Language:
- English
- Publication year:
- 2022
- Bibliographic description:
- Shen L., Zakharov Y., Niedźwiecki M., Gańcza A.: Finite-window RLS algorithms// SIGNAL PROCESSING -Vol. 198, (2022), s.108599-
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.sigpro.2022.108599
- Sources of funding:
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- Statutory activity/subsidy
- Project Generalized Savitzky-Golay filters for identification and smoothing of nonstationary processes
- The work of Y. Zakharov was supported in part by the U.K. EPSRC through Grants EP/V009591/1 and EP/R003297/1.
- Verified by:
- Gdańsk University of Technology
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