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<p>MIMO technology can transmit orthogonal waveforms and achieve virtual array aperture gain, providing higher angle measurement accuracy. It has been widely applied in the field of target angle estimation. However, achieving a balance between angle estimation accuracy and computational complexity in complex underwater environments remains challenging, and effective strategies are needed to mitigate the impact of symmetrical noise. This paper proposes the method for bistatic MIMO sonar based on the reduced-order regularized multi-measurement vector Focal Underdetermined System Solver ( RD-MFOCUSS) under Toeplitz symmetric noise. First, the signal covariance matrix is reconstructed by using the imaginary Toeplitz Hermitian transform, and the difference operation is performed with the original covariance matrix to eliminate the influence of noise. Next, according to the structural characteristics of the difference covariance matrix, the direction of departure ( DOD) and the direction of arrival ( DOA) are separated to reduce the search complexity. Finally, the sparse signal is reconstructed using RD-MFOCUSS, thereby achieving the estimation of the target’s DOA and DOD. The proposed approach effectively improves the imbalance between computational complexity and estimation accuracy compared to existing methods. It also performs well with colored noise and target angle estimation under limited measurement data. The effectiveness of the proposed method is validated through numerical simulations.</p>

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Joint DOD and DOA estimation for bistatic MIMO sonar based on reduced-order regularized MFOCUSS

  • 期刊论文

  • 发表日期：

    2025年1月11日

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  • Xuefei Ma,
  • Yuanhui Xiang,
  • Haifeng Zhu,
  • Rahim Khan,
  • Yanni Wu,
  • Chen Wang,
  • Wanyan Wang,
  • Hengliang Wu,
  • Zhiqiang Wu

摘要

MIMO technology can transmit orthogonal waveforms and achieve virtual array aperture gain, providing higher angle measurement accuracy. It has been widely applied in the field of target angle estimation. However, achieving a balance between angle estimation accuracy and computational complexity in complex underwater environments remains challenging, and effective strategies are needed to mitigate the impact of symmetrical noise. This paper proposes the method for bistatic MIMO sonar based on the reduced-order regularized multi-measurement vector Focal Underdetermined System Solver ( RD-MFOCUSS) under Toeplitz symmetric noise. First, the signal covariance matrix is reconstructed by using the imaginary Toeplitz Hermitian transform, and the difference operation is performed with the original covariance matrix to eliminate the influence of noise. Next, according to the structural characteristics of the difference covariance matrix, the direction of departure ( DOD) and the direction of arrival ( DOA) are separated to reduce the search complexity. Finally, the sparse signal is reconstructed using RD-MFOCUSS, thereby achieving the estimation of the target’s DOA and DOD. The proposed approach effectively improves the imbalance between computational complexity and estimation accuracy compared to existing methods. It also performs well with colored noise and target angle estimation under limited measurement data. The effectiveness of the proposed method is validated through numerical simulations.