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Robust Sparse Bayesian Two-Dimensional Direction-of-Arrival Estimation with Gain-Phase Errors.

Xu Jin1, Xuhu Wang1,2, Yujun Hou1

  • 1School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China.

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|December 9, 2023
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Summary
This summary is machine-generated.

This study introduces a robust sparse Bayesian method for direction-of-arrival (DOA) estimation using L-shaped arrays, effectively handling gain-phase errors. The new approach enhances DOA accuracy and angular resolution for incident signals.

Keywords:
L-shaped arraygain-phase errorssparse Bayesian learningsparse signal reconstructiontwo-dimensional direction-of-arrival (2D DOA)

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Area of Science:

  • Signal Processing
  • Array Signal Processing

Background:

  • Gain-phase errors degrade Direction-of-Arrival (DOA) estimation performance.
  • Accurate DOA estimation is crucial in various applications like radar and sonar.

Purpose of the Study:

  • To propose a robust sparse Bayesian 2D DOA estimation method for L-shaped sensor arrays.
  • To mitigate the impact of gain-phase errors on DOA estimation accuracy.

Main Methods:

  • Introduced an auxiliary angle to convert 2D DOA to two 1D problems.
  • Constructed a sparse representation model using cross-correlation covariance matrix submatrices.
  • Employed Expectation Maximization and Sparse Bayesian Learning for iterative parameter estimation.

Main Results:

  • The method effectively estimates both azimuth and elevation angles.
  • Achieved improved accuracy and angular resolution in DOA estimation.
  • Demonstrated robust performance in the presence of gain-phase errors.

Conclusions:

  • The proposed sparse Bayesian method offers a robust solution for 2D DOA estimation with L-shaped arrays.
  • It significantly enhances estimation accuracy and angular resolution by addressing gain-phase errors.
  • The auxiliary angle transformation simplifies the estimation process.