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<p>Sensor failures in sparse arrays have become a critical challenge for Direction of Arrival (DOA) estimation, as missing sensors create holes in the difference co-array structure and significantly degrade estimation performance. However, traditional matrix completion methods exhibit poor performance under low signal-to-noise ratio (SNR) and high sensor failure rate conditions. To address this problem, a Truncated Quadratic Norm with Toeplitz-Hermitian constraints (TQN-TH) algorithm for robust covariance matrix completion is proposed in this paper. The proposed method leverages the difference co-array property to mitigate the impact of sensor failures, employs TQN regularization to achieve superior rank approximation, and incorporates positive semi-definiteness (PSD), Toeplitz, and Hermitian structural constraints through an efficient Alternating Direction Method of Multipliers (ADMM) optimization framework. Finally, the MUSIC algorithm is applied to the recovered covariance matrix for DOA estimation. Extensive experimental results demonstrate that the proposed algorithm exhibits significant advantages over existing methods under various conditions, providing an effective solution for sensor failure problems in practical array signal processing applications.</p>

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Robust DOA Estimation for Sparse Arrays under Sensor Failures via Structured Matrix Completion

  • 期刊论文

  • 发表日期：

    2025年7月30日

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  • Yang Wu,
  • Hui Cao,
  • Jialiang Zhang,
  • Kehao Wang

摘要

Sensor failures in sparse arrays have become a critical challenge for Direction of Arrival (DOA) estimation, as missing sensors create holes in the difference co-array structure and significantly degrade estimation performance. However, traditional matrix completion methods exhibit poor performance under low signal-to-noise ratio (SNR) and high sensor failure rate conditions. To address this problem, a Truncated Quadratic Norm with Toeplitz-Hermitian constraints (TQN-TH) algorithm for robust covariance matrix completion is proposed in this paper. The proposed method leverages the difference co-array property to mitigate the impact of sensor failures, employs TQN regularization to achieve superior rank approximation, and incorporates positive semi-definiteness (PSD), Toeplitz, and Hermitian structural constraints through an efficient Alternating Direction Method of Multipliers (ADMM) optimization framework. Finally, the MUSIC algorithm is applied to the recovered covariance matrix for DOA estimation. Extensive experimental results demonstrate that the proposed algorithm exhibits significant advantages over existing methods under various conditions, providing an effective solution for sensor failure problems in practical array signal processing applications.