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

Radu-Andrei Otopeleanu1,2, Constantin Paleologu1, Jacob Benesty3

  • 1Department of Telecommunications, National University of Science and Technology POLITEHNICA Bucharest, 060042 Bucharest, Romania.

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Summary
This summary is machine-generated.

This study introduces a computationally efficient data-reuse technique for the regularized Recursive Least-Squares (RLS) algorithm. The enhanced adaptive filtering method improves convergence and robustness in challenging conditions like noisy environments.

Keywords:
adaptive filtersdata-reuseecho cancellationrecursive least-squares (RLS) algorithmregularizationrobustnesssystem identification

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Area of Science:

  • Signal Processing
  • Adaptive Filtering
  • System Identification

Background:

  • Recursive Least-Squares (RLS) algorithm is effective for adaptive filtering and system identification.
  • RLS offers fast convergence but can lack robustness in noisy conditions.
  • Convergence and robustness are often conflicting performance criteria.

Purpose of the Study:

  • To develop a computationally efficient data-reuse technique for the regularized RLS algorithm.
  • To enhance the robustness of the RLS algorithm in noisy environments.
  • To achieve a compromise between convergence rate and robustness.

Main Methods:

  • Implementation of a regularized RLS algorithm with a data-reuse technique.
  • Utilizing an equivalent single step for data-reuse to improve computational efficiency.
  • Involvement of variable-regularized algorithms with time-dependent regularization parameters.
  • Testing the algorithms in echo cancellation applications.

Main Results:

  • The developed data-reuse regularized RLS algorithms demonstrate reliable performance.
  • The approach provides a good compromise between convergence and robustness.
  • Effective control is achieved in challenging conditions, including noisy environments.

Conclusions:

  • The proposed computationally efficient data-reuse regularized RLS algorithms offer improved performance.
  • These algorithms are suitable for adaptive filtering applications, particularly in echo cancellation.
  • The findings support the theoretical benefits of the enhanced RLS approach for system identification.