Acoustic DOA Estimation in Underwater Environments by Integrating Spatial Domain Wiener Filtering and Convolutional Neural Networks

XING Chuanxi; HUANG Tinglong; TAN Guangzhi; LI Weiqiang

doi:10.11999/JEIT250141

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XING Chuanxi, HUANG Tinglong, TAN Guangzhi, LI Weiqiang. Acoustic DOA Estimation in Underwater Environments by Integrating Spatial Domain Wiener Filtering and Convolutional Neural Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250141

Citation:

XING Chuanxi, HUANG Tinglong, TAN Guangzhi, LI Weiqiang. Acoustic DOA Estimation in Underwater Environments by Integrating Spatial Domain Wiener Filtering and Convolutional Neural Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250141

Citation:

XING Chuanxi, HUANG Tinglong, TAN Guangzhi, LI Weiqiang. Acoustic DOA Estimation in Underwater Environments by Integrating Spatial Domain Wiener Filtering and Convolutional Neural Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250141

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Acoustic DOA Estimation in Underwater Environments by Integrating Spatial Domain Wiener Filtering and Convolutional Neural Networks

doi: 10.11999/JEIT250141 cstr: 32379.14.JEIT250141

1.
College of Electrical and Information Technology, Yunnan Minzu University, Kunming 650504, China
2.
Yunnan Key Laboratory of Unmanned Autonomous System, Kunming 650504, China

Funds: The National Natural Science Foundation of China(61761048), The Yunnan Provincial Basic Research Project (20210AT070132)

Received Date: 2025-03-10
Rev Recd Date: 2025-07-29

Available Online: 2025-08-05

Abstract

Abstract

Objective Human activities in shallow-sea regions have increased with the progressive development and utilization of marine environments. However, interference from noise sources such as water currents, vessels, and marine organisms substantially reduces the Signal-to-Noise Ratio (SNR) of received signals, leading to performance degradation in conventional Direction of Arrival (DOA) estimation algorithms. These limitations affect accurate target localization and detection, which are essential for underwater acoustic applications. This paper proposes a method that combines Wiener filtering with deep learning, specifically an improved Convolutional Neural Network (CNN), to enhance the robustness and accuracy of DOA estimation in shallow-sea environments by addressing challenges such as noise interference and grid mismatch. Methods The proposed algorithm integrates Wiener filtering and deep learning to optimize DOA estimation. First, Wiener filtering is applied to the covariance matrix of received signals to reduce noise. Considering the complexity of underwater acoustic environments, this preprocessing step is essential for preserving spatial signal features and enhancing the effectiveness of network training. Second, an improved CNN architecture is designed to perform both classification and regression tasks, enabling accurate DOA angle estimation while correcting off-grid errors. A fractional label correction strategy is introduced to mitigate grid mismatch, allowing prediction errors to be compensated and estimated angles to align more closely with true values. The network is trained using a large-scale dataset of simulated underwater acoustic signals, and its feasibility is verified through real-world sea trial data. Results and Discussions The proposed method is validated using both simulation data and sea trial data, demonstrating superior performance under low SNR conditions. Simulation results show that incorporating Wiener filtering improves overall network performance by 25.20% (Fig. 4). The proposed algorithm achieves lower estimation errors in low SNR environments (Fig. 4, Fig. 5). Compared with the MUSIC and ESPRIT algorithms, the Root Mean Square Error (RMSE) is reduced by 93.42% and 92.14%, respectively, at an SNR of –20 dB. Moreover, as the SNR increases, the algorithm exhibits a gradual reduction in estimation error (Fig. 6). In sea trial experiments, the algorithm accurately identifies target azimuths and achieves lower estimation errors compared to conventional methods (Fig. 9, Table 1). These results indicate that the proposed method meets the requirements for robustness and accuracy in practical underwater acoustic applications. Conclusions This paper proposes a method for direction estimation in shallow-water environments that integrates Wiener filtering with a deep learning-based CNN to address the performance degradation caused by low SNR conditions in conventional algorithms. The combination of noise reduction preprocessing with CNN model training enhances both network efficiency and estimation accuracy. Simulation and sea trial experiments yield the following results: (1) Overall network performance is improved by 25.20% through Wiener filtering optimization; (2) The improved binary cross-entropy loss function enables the network to perform both classification and regression tasks for DOA estimation; (3) The algorithm demonstrates higher robustness to noise interference and grid mismatch, particularly under low SNR and limited snapshot conditions, making it suitable for practical deployment. However, the network is trained using datasets with known source numbers. Future research will focus on network designs that accommodate more diverse source conditions, including blind source scenarios and adaptive noise reduction models.
- DOA estimation,
- Deep learning,
- Wiener filtering,
- Grid mismatch

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References(24)

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