Robust Regularized Recursive Least-Squares Algorithm Based on Third-Order Tensor Decomposition

doi:10.3390/a18120768

Robust Regularized Recursive Least-Squares Algorithm Based on Third-Order Tensor Decomposition

2025-12-05

0

PRE

AI

R

Radu-Andrei Otopeleanu

C

Constantin Paleologu *

J

Jacob Benesty

C

Cristian-Lucian Stanciu

DOI：10.3390/a18120768

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Abstract

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The decomposition-based adaptive filtering algorithms have recently gained increasing interest due to their capability to reduce the parameter space. In this context, the third-order tensor (TOT) decomposition technique reformulates the conventional approach that involves a single (usually long) adaptive filter by using a combination of three shorter filters via the Kronecker product. This leads to a twofold gain in terms of both performance and complexity. Thus, it can be applied efficiently when operating with more complex algorithms, like the recursive least-squares (RLS) approach. In this paper, we develop an RLS-TOT algorithm with improved robustness features due to a novel regularization method that considers the contribution of the external noise and the so-called model uncertainties (which are related to the system). Simulation results obtained in the framework of echo cancelation support the performance of the proposed algorithm, which outperforms the existing RLS-TOT counterparts, as well as the conventional RLS algorithm that uses the specific regularization technique.

Keywords:

adaptive filters

echo cancelation

nearest Kronecker product

recursive least-squares (RLS) algorithm

regularization

system identification

third-order tensor decomposition

Robust Regularized Recursive Least-Squares Algorithm Based on Third-Order Tensor Decomposition

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