Single Vector Hydrophone DOA Estimation: Leveraging Deep Learning with CNN-CBAM
In recent years, single vector hydrophones have attracted widespread attention in target direction estimation due to their compact design and advantages in complex underwater acoustic environments. However, traditional direction of arrival (DOA) estimation algorithms often struggle to maintain high...
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| Format: | Article |
| Language: | English |
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Institute of Fundamental Technological Research Polish Academy of Sciences
2025-06-01
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| Series: | Archives of Acoustics |
| Subjects: | |
| Online Access: | https://acoustics.ippt.pan.pl/index.php/aa/article/view/4138 |
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| author | Fanyu ZENG Yaning HAN Hongyuan YANG Dapeng YANG Fan ZHENG |
| author_facet | Fanyu ZENG Yaning HAN Hongyuan YANG Dapeng YANG Fan ZHENG |
| author_sort | Fanyu ZENG |
| collection | DOAJ |
| description | In recent years, single vector hydrophones have attracted widespread attention in target direction estimation due to their compact design and advantages in complex underwater acoustic environments. However, traditional direction of arrival (DOA) estimation algorithms often struggle to maintain high accuracy in nonstationary noise conditions. This study proposes the novel DOA estimation method based on a convolutional neural network (CNN) and the convolutional block attention module (CBAM). By inputting the covariance matrix of the received signal into the neural network and integrating the CBAM module, this method enhances the model’s sensitivity to critical features. The CBAM module leverages channel and spatial attention mechanisms to adaptively focus on essential information, effectively suppressing noise interference and improving directional accuracy. Specifically, CBAM improves the model’s focus on subtle directional cues in noisy environments, suppressing irrelevant interference while amplifying essential signal components, which is crucial for an accurate DOA estimation. Experimental results under various signal-to-noise ratio (SNR) conditions validate the method’s effectiveness, demonstrating superior noise resistance and estimation precision, providing a robust and efficient solution for underwater acoustic target localization. |
| format | Article |
| id | doaj-art-a3f2bb838aec4d5fa49eccc9b555df96 |
| institution | Kabale University |
| issn | 0137-5075 2300-262X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Institute of Fundamental Technological Research Polish Academy of Sciences |
| record_format | Article |
| series | Archives of Acoustics |
| spelling | doaj-art-a3f2bb838aec4d5fa49eccc9b555df962025-08-20T03:37:05ZengInstitute of Fundamental Technological Research Polish Academy of SciencesArchives of Acoustics0137-50752300-262X2025-06-0150218719810.24425/aoa.2025.1536593704Single Vector Hydrophone DOA Estimation: Leveraging Deep Learning with CNN-CBAMFanyu ZENG0Yaning HAN1Hongyuan YANG2Dapeng YANG3Fan ZHENG4Key Laboratory of Geophysical Exploration Equipment, Ministry of Education, College of Instrumentation and Electrical Engineering, Jilin UniversityKey Laboratory of Geophysical Exploration Equipment, Ministry of Education, College of Instrumentation and Electrical Engineering, Jilin UniversityKey Laboratory of Geophysical Exploration Equipment, Ministry of Education, College of Instrumentation and Electrical Engineering, Jilin UniversityKey Laboratory of Geophysical Exploration Equipment, Ministry of Education, College of Instrumentation and Electrical Engineering, Jilin UniversityKey Laboratory of Geophysical Exploration Equipment, Ministry of Education, College of Instrumentation and Electrical Engineering, Jilin UniversityIn recent years, single vector hydrophones have attracted widespread attention in target direction estimation due to their compact design and advantages in complex underwater acoustic environments. However, traditional direction of arrival (DOA) estimation algorithms often struggle to maintain high accuracy in nonstationary noise conditions. This study proposes the novel DOA estimation method based on a convolutional neural network (CNN) and the convolutional block attention module (CBAM). By inputting the covariance matrix of the received signal into the neural network and integrating the CBAM module, this method enhances the model’s sensitivity to critical features. The CBAM module leverages channel and spatial attention mechanisms to adaptively focus on essential information, effectively suppressing noise interference and improving directional accuracy. Specifically, CBAM improves the model’s focus on subtle directional cues in noisy environments, suppressing irrelevant interference while amplifying essential signal components, which is crucial for an accurate DOA estimation. Experimental results under various signal-to-noise ratio (SNR) conditions validate the method’s effectiveness, demonstrating superior noise resistance and estimation precision, providing a robust and efficient solution for underwater acoustic target localization.https://acoustics.ippt.pan.pl/index.php/aa/article/view/4138single vector hydrophonedirection of arrival (doa)convolutional neural network (cnn)convolutional block attention module (cbam)noise resistance |
| spellingShingle | Fanyu ZENG Yaning HAN Hongyuan YANG Dapeng YANG Fan ZHENG Single Vector Hydrophone DOA Estimation: Leveraging Deep Learning with CNN-CBAM Archives of Acoustics single vector hydrophone direction of arrival (doa) convolutional neural network (cnn) convolutional block attention module (cbam) noise resistance |
| title | Single Vector Hydrophone DOA Estimation: Leveraging Deep Learning with CNN-CBAM |
| title_full | Single Vector Hydrophone DOA Estimation: Leveraging Deep Learning with CNN-CBAM |
| title_fullStr | Single Vector Hydrophone DOA Estimation: Leveraging Deep Learning with CNN-CBAM |
| title_full_unstemmed | Single Vector Hydrophone DOA Estimation: Leveraging Deep Learning with CNN-CBAM |
| title_short | Single Vector Hydrophone DOA Estimation: Leveraging Deep Learning with CNN-CBAM |
| title_sort | single vector hydrophone doa estimation leveraging deep learning with cnn cbam |
| topic | single vector hydrophone direction of arrival (doa) convolutional neural network (cnn) convolutional block attention module (cbam) noise resistance |
| url | https://acoustics.ippt.pan.pl/index.php/aa/article/view/4138 |
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