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Sparse deconvolution beamforming with non-negative L1-αL2 regularization for acoustic source localization.

Zhaohui Du1, Yinan Zhu1, Han Zhang2

  • 1School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China.

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|January 24, 2025
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Summary
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A new sparse deconvolution localization method (FFT-L1ML2) improves sound source accuracy by using L1-αL2 regularization. This advanced technique offers better energy concentration and reduces computational costs compared to existing methods.

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Area of Science:

  • Acoustics
  • Signal Processing
  • Computational Mathematics

Background:

  • Sparse deconvolution beamforming methods are crucial for sound source localization.
  • Existing methods may lack sufficiently accurate sparse descriptions, limiting localization precision.
  • Enhancing the sparse representation is key to improving localization performance.

Purpose of the Study:

  • To propose a novel sparse deconvolution localization method, FFT-L1ML2, utilizing non-convex L1-αL2 regularization.
  • To enhance sound source localization accuracy by better approximating the L0 norm.
  • To develop an efficient optimization solver for the proposed model.

Main Methods:

  • Implementation of a sparse deconvolution localization method (FFT-L1ML2).
  • Application of non-convex L1-αL2 regularization to approximate the L0 norm for improved sparse representation.
  • Development of an optimization solver using forward gradient descent and backward proximal operator.

Main Results:

  • The FFT-L1ML2 method demonstrates superior localization accuracy compared to existing techniques.
  • Improved energy concentration and significant reduction in pseudo sound sources were observed.
  • The method shows effectiveness in both simulated and experimental scenarios.
  • Reduced computational cost was noted as an advantage.

Conclusions:

  • The proposed FFT-L1ML2 method offers a significant advancement in sparse deconvolution-based sound source localization.
  • The L1-αL2 regularization effectively captures the sparse structure of sound sources, leading to enhanced accuracy.
  • FFT-L1ML2 provides a more effective and computationally efficient alternative for acoustic source identification.