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.
Authors
- Lu Shen,
- Yuriy Zakharov,
- prof. dr hab. inż. Maciej Niedźwiecki link open in new tab ,
- dr inż. Artur Gańcza link open in new tab
Additional information
- DOI
- Digital Object Identifier link open in new tab 10.1016/j.sigpro.2022.108599
- Category
- Publikacja w czasopiśmie
- Type
- artykuły w czasopismach
- Language
- angielski
- Publication year
- 2022
Source: MOSTWiedzy.pl - publication "Finite-window RLS algorithms" link open in new tab