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Robust Sparse Bayesian Learning Scheme for DOA Estimation with Non-Circular Sources

Author

Listed:
  • Linlu Jian

    (State Key Laboratory of Marine Resource Utilization in South China Sea, School of Information and Communication Engineering, Hainan University, Haikou 570228, China)

  • Xianpeng Wang

    (State Key Laboratory of Marine Resource Utilization in South China Sea, School of Information and Communication Engineering, Hainan University, Haikou 570228, China)

  • Jinmei Shi

    (College of Information Engineering, Hainan Vocational University of Science and Technology, Haikou 571158, China)

  • Xiang Lan

    (State Key Laboratory of Marine Resource Utilization in South China Sea, School of Information and Communication Engineering, Hainan University, Haikou 570228, China)

Abstract

In this paper, a robust DOA estimation scheme based on sparse Bayesian learning (SBL) for non-circular signals in impulse noise and mutual coupling (MC) is proposed. Firstly, the Toeplitz property of the MC matrix is used to eliminate the effect of array MC, and the array aperture is extended by using the properties of the non-circular signal. To eliminate the effect of impulse noise, the outlier part of the impulse noise is reconstructed together with the original signal in the signal matrix, and the DOA coarse estimation is obtained by balancing the accuracy and efficiency of parameter estimation using the alternating SBL update algorithm. Finally, a one-dimensional search is used in the vicinity of the searched spectral peaks to achieve a high-precision DOA estimation. The effectiveness and robustness of the algorithm for dealing with the above errors are demonstrated by extensive simulations.

Suggested Citation

  • Linlu Jian & Xianpeng Wang & Jinmei Shi & Xiang Lan, 2022. "Robust Sparse Bayesian Learning Scheme for DOA Estimation with Non-Circular Sources," Mathematics, MDPI, vol. 10(6), pages 1-14, March.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:6:p:923-:d:770638
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