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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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A Sparse Conjugate Gradient Adaptive Filter.

Ching-Hua Lee1, Bhaskar D Rao1, Harinath Garudadri1

  • 1Department of Electrical and Computer Engineering, University of California, San Diego, CA 92093 USA.

IEEE Signal Processing Letters
|August 4, 2020
PubMed
Summary
This summary is machine-generated.

We developed a new adaptive filtering algorithm called Sparsity-promoting Conjugate Gradient (SCG) for estimating sparse system responses. SCG shows better performance and faster convergence than current methods.

Keywords:
Affine scalingconjugate gradientiterative reweightingsparse adaptive filtersparse signal recovery

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Area of Science:

  • Signal Processing
  • Adaptive Filtering
  • Sparse Signal Recovery

Background:

  • Online estimation of system responses is crucial in various signal processing applications.
  • Existing adaptive filtering algorithms struggle with systems exhibiting sparsity.
  • Sparse signal recovery often employs iterative reweighting methods.

Purpose of the Study:

  • To propose a novel conjugate gradient (CG) adaptive filtering algorithm for online estimation of sparse system responses.
  • To develop the Sparsity-promoting Conjugate Gradient (SCG) algorithm.
  • To enhance the algorithm's ability to leverage existing sparsity without compromising optimization.

Main Methods:

  • Development of the Sparsity-promoting Conjugate Gradient (SCG) algorithm.
  • Application of iterative reweighting techniques from sparse signal recovery.
  • Introduction of an affine scaling transformation strategy within the reweighting framework.
  • Allowing a zero sparsity regularization coefficient for enhanced flexibility.

Main Results:

  • The SCG algorithm demonstrates improved convergence properties compared to existing methods.
  • SCG exhibits enhanced steady-state performance.
  • The proposed affine scaling strategy enables effective utilization of system response sparsity.

Conclusions:

  • The novel SCG algorithm offers a significant advancement in adaptive filtering for sparse systems.
  • SCG provides a robust and efficient solution for online system response estimation.
  • The algorithm's design effectively balances sparsity promotion and optimization performance.