DOA estimation for underwater acoustic array signals based on eigenvalue weighting

LI Weiqiang; XING Chuanxi; TAN Guangzhi; HUANG Tinglong

doi:10.11714/acta.snus.ZR20250236

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DOA estimation for underwater acoustic array signals based on eigenvalue weighting

Research articles | 更新时间：2026-05-22

- DOA estimation for underwater acoustic array signals based on eigenvalue weighting
- Acta Scientiarum Naturalium Universitatis Sunyatseni Vol. 65, Issue 3, Pages: 112-120(2026)
- 作者机构：
  
  云南民族大学电气信息工程学院 / 云南省无人自主系统重点实验室，云南昆明 650504
- 作者简介：
- 基金信息：
- DOI：10.11714/acta.snus.ZR20250236
  CLC： TN911.7
- Received：06 November 2025，
  
  Accepted：08 December 2025，
  
  Online First：23 January 2026，
  
  Published：25 May 2026
- 稿件说明：
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李维强,邢传玺,谈光枝等.基于特征值加权的稳健水声阵列信号DOA估计[J].中山大学学报(自然科学版)(中英文),2026,65(03):112-120.

LI Weiqiang,XING Chuanxi,TAN Guangzhi,et al.DOA estimation for underwater acoustic array signals based on eigenvalue weighting[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2026,65(03):112-120.
李维强,邢传玺,谈光枝等.基于特征值加权的稳健水声阵列信号DOA估计[J].中山大学学报(自然科学版)(中英文),2026,65(03):112-120. DOI： 10.11714/acta.snus.ZR20250236.

LI Weiqiang,XING Chuanxi,TAN Guangzhi,et al.DOA estimation for underwater acoustic array signals based on eigenvalue weighting[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2026,65(03):112-120. DOI： 10.11714/acta.snus.ZR20250236.

摘要

提出了一种融合Toeplitz重构与特征值加权降噪的嵌套阵列离格稀疏贝叶斯水声信号方位估计方法。算法通过虚拟域映射与重构获得满秩协方差矩阵，并对信号子空间特征值加权以抑制噪声并保留有效信息；进而构建离格稀疏表示模型，利用贝叶斯学习实现最大后验估计。仿真与海试实验表明，该方法仅需6个物理阵元即可估计11个信源，在低快拍和近距离多目标场景下仍保持高分辨率与稳定性；当信噪比为

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1.52400005

2.28600001

15 dB时，所提算法的RMSE较同阵列下的MUSIC与ESPRIT算法分别提升了53.11%和60.04%。所提算法能有效利用虚拟阵列自由度、抑制噪声干扰，实现低信噪比、小快拍数下更精确的DOA估计，鲁棒性更优。

Abstract

An off-grid sparse Bayesian method for underwater acoustic signals using nested arrays is proposed. The method integrates Toeplitz reconstruction and eigenvalue-weighted denoising. It constructs a fu

ll-rank covariance matrix through virtual array mapping and reconstruction，then applies eigenvalue weighting to the signal subspace to suppress noise while preserving essential information. And off-grid sparse representation model is solved by Bayesian learning for maximum a posteriori estimation. Simulation and sea trial results demonstrate that the method can estimate up to 11 sources using only 6 physical sensor elements. It maintains high resolution and stability even in scenarios with a low number of snapshots and multiple closely spaced targets. Furthermore，at a signal-to-noise ratio（SNR） of

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5.84200001

2.70933342

dB， the estimation performance of the algorithm shows an improvement of 53.11% and 60.04%，compared to MUSIC and ESPRIT algorithms under the same array configuration. By effectively leveraging the degrees of freedom offered by the virtual array and suppressing noise interference，the proposed algorithm achieves more accurate direction of arrival（DOA） estimation under conditions of low SNR and a small number of snapshots，demonstrating superior robustness.

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references

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Related Author

LI Weiqiang

XING Chuanxi

TAN Guangzhi

HUANG Tinglong

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College of Electrical and Informaion Technology，Yunnan Minzu University / Yunnan Key Laborratory of Unmanned Autonomous System

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