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An Enhanced Affine Projection Algorithm Based on the Adjustment of Input-Vector Number.

Jaewook Shin1, Jeesu Kim2,3, Tae-Kyoung Kim4

  • 1Department of Electronic Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea.

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|March 25, 2022
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Summary
This summary is machine-generated.

This study introduces an enhanced affine projection algorithm (APA) that improves filter convergence and reduces estimation errors. By dynamically adjusting the input-vector number, the new APA offers superior performance over existing methods.

Keywords:
adaptive filteradjustmentaffine projection algorithmconvergence ratefilter performanceinput-vector numbersteady-state estimation error

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Area of Science:

  • Signal Processing
  • Adaptive Filtering

Background:

  • Adaptive filters are crucial for system identification and noise cancellation.
  • Conventional Affine Projection Algorithms (APA) face limitations in convergence speed and steady-state error.

Purpose of the Study:

  • To propose an enhanced Affine Projection Algorithm (APA) for improved filter performance.
  • To increase convergence rate and decrease steady-state estimation error simultaneously.

Main Methods:

  • Dynamically adjusting the input-vector number of the APA at each iteration.
  • Utilizing a pseudo-fractional method to relax the integer constraint on the input-vector number.
  • Comparing averages of accumulated squared errors to guide input-vector number adjustments.

Main Results:

  • The proposed APA demonstrates a reduced steady-state estimation error compared to existing APA-type filters.
  • Simulation results validate the enhanced performance across various scenarios.
  • The algorithm achieves faster convergence and lower estimation error.

Conclusions:

  • The enhanced APA effectively improves filter performance by optimizing the input-vector number.
  • The pseudo-fractional method allows for more precise updates, leading to better results.
  • This algorithm offers a significant advancement in adaptive filtering techniques.