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  3. Super-resolution Direction-of-arrival Estimation Based On Generalized Eigendecomposition And Sparse Reconstruction Under Near-field Interference.
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  3. Super-resolution Direction-of-arrival Estimation Based On Generalized Eigendecomposition And Sparse Reconstruction Under Near-field Interference.

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Super-resolution direction-of-arrival estimation based on generalized eigendecomposition and sparse reconstruction

Can Tang1,2, Duo Zhai1, Bo Zhang1

  • 1State Key Laboratory of Acoustics and Marine Information, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China.

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|January 13, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel generalized eigenvalue decomposition method to enhance super-resolution direction-of-arrival estimation. The technique effectively mitigates near-field interference, improving accuracy under challenging signal conditions.

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Area of Science:

  • Signal Processing
  • Array Signal Processing
  • Electromagnetics

Background:

  • Super-resolution sparse recovery methods struggle with strong near-field interference.
  • Existing solutions like dictionary augmentation or pre-filtering have limitations, causing basis coherence or signal distortion.

Purpose of the Study:

  • To develop a robust preprocessing method for direction-of-arrival estimation in the presence of strong near-field interference.
  • To overcome limitations of current sparse recovery techniques in challenging environments.

Main Methods:

  • A novel preprocessing method based on generalized eigenvalue decomposition is proposed.
  • The method employs a matrix filtering concept to maximize the power ratio between desired and interference subspaces.
  • It extends optimal beamforming to a matrix filtering framework, balancing interference suppression and target preservation.

Main Results:

  • The proposed method significantly improves super-resolution direction-of-arrival estimation performance under strong near-field interference.
  • It demonstrates effectiveness even with limited snapshots and low signal-to-noise ratios.
  • Simulation and experimental results validate the method's superiority over existing approaches.

Conclusions:

  • The generalized eigenvalue decomposition preprocessing method offers a computationally efficient and effective solution for near-field interference mitigation.
  • It enables robust super-resolution direction-of-arrival estimation in practical scenarios.
  • The approach provides a balanced trade-off, preserving target signals while suppressing interference.