Robust Data-Reuse Regularized Recursive Least-Squares Algorithms for System Identification Applications

The recursive least-squares (RLS) algorithm stands out as an appealing choice in adaptive filtering applications related to system identification problems. This algorithm is able to provide a fast convergence rate for various types of input signals, which represents its main asset. In the current pa...

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Published in:Sensors (Basel, Switzerland) Vol. 25; no. 16; p. 5017
Main Authors: Otopeleanu, Radu-Andrei, Paleologu, Constantin, Benesty, Jacob, Dogariu, Laura-Maria, Stanciu, Cristian-Lucian, Ciochină, Silviu
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 13.08.2025
MDPI
Subjects:
Accuracy
adaptive filters
Algorithms
data-reuse
echo cancellation
Identification
recursive least-squares (RLS) algorithm
regularization
robustness
Signal processing
Romania
adaptive filters
data-reuse
echo cancellation
regularization
robustness
recursive least-squares (RLS) algorithm
system identification
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:The recursive least-squares (RLS) algorithm stands out as an appealing choice in adaptive filtering applications related to system identification problems. This algorithm is able to provide a fast convergence rate for various types of input signals, which represents its main asset. In the current paper, we focus on the regularized version of the RLS algorithm, which also owns improved robustness in noisy conditions. Since convergence and robustness are usually conflicting criteria, the data-reuse technique is used to achieve a proper compromise between these performance features. In this context, we develop a computationally efficient approach for the data-reuse process in conjunction with the regularized RLS algorithm, using an equivalent single step instead of multiple iterations (for data-reuse). In addition, different regularization techniques are involved, which lead to variable-regularized algorithms, with time-dependent regularization parameters. This allows a better control in different challenging conditions, including noisy environments and other external disturbances. The resulting data-reuse regularized RLS algorithms are tested in the framework of echo cancellation, where the obtained results support the theoretical findings and indicate the reliable performance of these algorithms.
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Presented at the 2024 16th International Symposium on Electronics and Telecommunications (ISETC), Timişoara, Romania, 7–8 November 2024.
ISSN:1424-8220
1424-8220
DOI:10.3390/s25165017