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Updated: Nov 11, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Proportionate Adaptive Filtering Algorithms Derived Using an Iterative Reweighting Framework.

Ching-Hua Lee1, Bhaskar D Rao1, Harinath Garudadri1

  • 1The authors are with the Department of Electrical and Computer Engineering, University of California, San Diego, CA 92093 USA.

IEEE/ACM Transactions on Audio, Speech, and Language Processing
|March 29, 2021
PubMed
Summary
This summary is machine-generated.

New sparse adaptive filtering algorithms are derived using regularization techniques. These methods improve convergence properties and offer benefits in applications like acoustic echo cancellation.

Keywords:
affine scalingiterative reweightedproportionate adaptationsparse adaptive filtersparse signal recovery

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Area of Science:

  • Signal Processing
  • Machine Learning

Background:

  • Adaptive filtering is crucial for signal processing tasks.
  • Sparse signal recovery (SSR) techniques offer efficient solutions for systems with sparse characteristics.

Purpose of the Study:

  • To derive novel Least Mean Square (LMS)-type sparse adaptive filtering algorithms.
  • To enhance convergence properties of adaptive filters using sparsity-promoting regularization.

Main Methods:

  • Leveraging sparsity-promoting regularization techniques from SSR.
  • Mimicking iterative reweighted ℓ2 and ℓ1 SSR methods for objective function majorization.
  • Introducing an affine scaling transformation (AST) for gradient weighting.

Main Results:

  • The reweighting formulation naturally leads to an AST strategy, improving convergence.
  • Setting the regularization coefficient to zero yields Sparsity-promoting LMS (SLMS) and Sparsity-promoting Normalized LMS (SNLMS) algorithms.
  • SLMS and SNLMS algorithms enable proportionate adaptation for faster convergence when system sparsity is present.

Conclusions:

  • A rigorous framework is established for deriving proportionate adaptive filtering algorithms.
  • The derived algorithms are beneficial for applications with sparse underlying systems, such as acoustic echo and feedback cancellation.