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Sequential adaptive elastic net approach for single-snapshot source localization.

Muhammad Naveed Tabassum1, Esa Ollila1

  • 1Department of Signal Processing and Acoustics, Aalto University, P.O. Box 15400, FI-00076 Aalto, Finland.

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Summary
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This study introduces new algorithms for accurate direction-of-arrival (DoA) recovery from single-snapshot measurements using compressed beamforming. The proposed sequential adaptive elastic net (SAEN) method significantly enhances source recovery, especially in complex scenarios.

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Area of Science:

  • Signal Processing
  • Array Signal Processing
  • Sparse Signal Recovery

Background:

  • Direction-of-arrival (DoA) estimation is crucial in various applications.
  • Conventional methods struggle with single-snapshot and underdetermined models.
  • Compressed beamforming (CBF) offers a promising approach for sparse DoA recovery.

Purpose of the Study:

  • To develop efficient algorithms for accurate DoA recovery from single-snapshot measurements using CBF.
  • To introduce novel sparse signal recovery techniques for underdetermined linear regression models.
  • To enhance the performance of DoA estimation in challenging multi-source scenarios.

Main Methods:

  • Development of a complex-valued pathwise weighted elastic net (c-PW-WEN) algorithm.
  • Proposal of a sequential adaptive elastic net (SAEN) method utilizing adaptive weights.
  • Comparison with existing sparse recovery methods like LASSO, EN, and orthogonal matching pursuit.

Main Results:

  • SAEN demonstrates improved probability of exact support recovery compared to conventional methods.
  • The proposed algorithms achieve accurate DoA recovery from single-snapshot measurements.
  • SAEN's effectiveness is particularly notable in high mutual coherence and multi-target scenarios.

Conclusions:

  • The developed SAEN algorithm offers a significant advancement in DoA estimation accuracy.
  • Efficient and accurate source recovery is achievable even with limited measurement data.
  • The proposed methods provide robust solutions for complex signal environments.