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<p>The estimation of coherent signals in electromagnetic (EM) environments is constrained by a small number of snapshots and low signal-to-noise ratio (SNR). This paper presents an improved off-grid direction of arrival (DOA) estimation algorithm based on sparse Bayesian learning. The algorithm aims to enhance the accuracy of DOA estimation for coherent signals in EM environments with a small number of snapshots and a low SNR. The algorithm first employs a novel dimensionality reduction technique based on singular value decomposition (SVD) to construct a sparse signal representation model. Then, based on sparse Bayesian theory, the expectation–maximization algorithm is used to optimize parameters such as quantization error, noise power, and signal power, thereby improving DOA estimation accuracy. Simulation results demonstrate that, compared to subspace-based and on-grid direction-finding methods, the proposed algorithm achieves higher DOA estimation accuracy under conditions of low SNR, a small number of snapshots, and high signal correlation. Specifically, when the SNR exceeds − 4&#xa0;dB, the root mean square error of the proposed algorithm is less than 2° in scenarios with a small number of snapshots. Additionally, when the SNR is greater than − 6&#xa0;dB, the algorithm achieves a direction-finding success rate of over 90%. Furthermore, the algorithm maintains high performance even with correlated and fully coherent signals, demonstrating robustness to variations in the correlation coefficient.</p>

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Optimization algorithm for DOA estimation accuracy of coherent signal sources based on sparse Bayesian theory

  • 期刊论文

  • 发表日期：

    2025年1月28日

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  • Zhuqing Mei,
  • Xue Li,
  • Hengfeng Li,
  • Shuoyang Wang,
  • Yu Zheng,
  • Tian Liu,
  • Zhongsen Sun

摘要

The estimation of coherent signals in electromagnetic (EM) environments is constrained by a small number of snapshots and low signal-to-noise ratio (SNR). This paper presents an improved off-grid direction of arrival (DOA) estimation algorithm based on sparse Bayesian learning. The algorithm aims to enhance the accuracy of DOA estimation for coherent signals in EM environments with a small number of snapshots and a low SNR. The algorithm first employs a novel dimensionality reduction technique based on singular value decomposition (SVD) to construct a sparse signal representation model. Then, based on sparse Bayesian theory, the expectation–maximization algorithm is used to optimize parameters such as quantization error, noise power, and signal power, thereby improving DOA estimation accuracy. Simulation results demonstrate that, compared to subspace-based and on-grid direction-finding methods, the proposed algorithm achieves higher DOA estimation accuracy under conditions of low SNR, a small number of snapshots, and high signal correlation. Specifically, when the SNR exceeds − 4 dB, the root mean square error of the proposed algorithm is less than 2° in scenarios with a small number of snapshots. Additionally, when the SNR is greater than − 6 dB, the algorithm achieves a direction-finding success rate of over 90%. Furthermore, the algorithm maintains high performance even with correlated and fully coherent signals, demonstrating robustness to variations in the correlation coefficient.