Data-Reuse Recursive Least-Squares Algorithms

There are different strategies to improve the overall performance of the recursive least-squares (RLS) adaptive filter. In this letter, we focus on the data-reuse approach, aiming to improve the convergence rate/tracking of the algorithm by reusing the same set of data (i.e., the input and reference...

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Veröffentlicht in:IEEE signal processing letters Jg. 29; S. 752 - 756
Hauptverfasser: Paleologu, Constantin, Benesty, Jacob, Ciochina, Silviu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Adaptive filters
Algorithms
Computational complexity
Convergence
Covariance matrices
data-reuse
echo cancellation
fast RLS (FRLS) algorithm
Indexes
Jacobian matrices
Kalman filters
Least squares
recursive least-squares (RLS) algorithm
Reference signals
Reuse
Signal processing algorithms
tracking
ISSN:1070-9908, 1558-2361
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Zusammenfassung:There are different strategies to improve the overall performance of the recursive least-squares (RLS) adaptive filter. In this letter, we focus on the data-reuse approach, aiming to improve the convergence rate/tracking of the algorithm by reusing the same set of data (i.e., the input and reference signals) several times. First, we present a computationally efficient data-reuse RLS algorithm, which is the result of a low complexity implementation of the data-reuse process. Moreover, we extend the idea to the fast RLS algorithm. Simulations performed in the context of echo cancellation support the performance gain.
Bibliographie:ObjectType-Article-1
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content type line 14
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2022.3153207