Direction-of-Arrival Estimation Method Based on Neural Network with Temporal Structure for Underwater Acoustic Vector Sensor Array
Acoustic vector sensor (AVS) is a kind of sensor widely used in underwater detection. Traditional methods use the covariance matrix of the received signal to estimate the direction-of-arrival (DOA), which not only loses the timing structure of the signal but also has the problem of weak anti-noise a...
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| Format: | Article |
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MDPI AG
2023-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/10/4919 |
| _version_ | 1827740095515459584 |
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| author | Yangyang Xie Biao Wang |
| author_facet | Yangyang Xie Biao Wang |
| author_sort | Yangyang Xie |
| collection | DOAJ |
| description | Acoustic vector sensor (AVS) is a kind of sensor widely used in underwater detection. Traditional methods use the covariance matrix of the received signal to estimate the direction-of-arrival (DOA), which not only loses the timing structure of the signal but also has the problem of weak anti-noise ability. Therefore, this paper proposes two DOA estimation methods for underwater AVS arrays, one based on a long short-term memory network and attention mechanism (LSTM-ATT), and the other based on Transformer. These two methods can capture the contextual information of sequence signals and extract features with important semantic information. The simulation results show that the two proposed methods perform much better than the multiple signal classification (MUSIC) method, especially in the case of low signal-to-noise ratio (SNR), the DOA estimation accuracy has been greatly improved. The accuracy of the DOA estimation method based on Transformer is comparable to that of the DOA estimation method based on LSTM-ATT, but the computational efficiency is obviously better than that of the DOA estimation method based on LSTM-ATT. Therefore, the DOA estimation method based on Transformer proposed in this paper can provide a reference for fast and effective DOA estimation under low SNR. |
| first_indexed | 2024-03-11T03:20:13Z |
| format | Article |
| id | doaj.art-3a0c5bbd13ae430a95f57b7898f7b3b4 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T03:20:13Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3a0c5bbd13ae430a95f57b7898f7b3b42023-11-18T03:14:32ZengMDPI AGSensors1424-82202023-05-012310491910.3390/s23104919Direction-of-Arrival Estimation Method Based on Neural Network with Temporal Structure for Underwater Acoustic Vector Sensor ArrayYangyang Xie0Biao Wang1School of Naval Architecture and Ocean Engineering, Jiangsu University of Science and Technology, Zhenjiang 212100, ChinaSchool of Naval Architecture and Ocean Engineering, Jiangsu University of Science and Technology, Zhenjiang 212100, ChinaAcoustic vector sensor (AVS) is a kind of sensor widely used in underwater detection. Traditional methods use the covariance matrix of the received signal to estimate the direction-of-arrival (DOA), which not only loses the timing structure of the signal but also has the problem of weak anti-noise ability. Therefore, this paper proposes two DOA estimation methods for underwater AVS arrays, one based on a long short-term memory network and attention mechanism (LSTM-ATT), and the other based on Transformer. These two methods can capture the contextual information of sequence signals and extract features with important semantic information. The simulation results show that the two proposed methods perform much better than the multiple signal classification (MUSIC) method, especially in the case of low signal-to-noise ratio (SNR), the DOA estimation accuracy has been greatly improved. The accuracy of the DOA estimation method based on Transformer is comparable to that of the DOA estimation method based on LSTM-ATT, but the computational efficiency is obviously better than that of the DOA estimation method based on LSTM-ATT. Therefore, the DOA estimation method based on Transformer proposed in this paper can provide a reference for fast and effective DOA estimation under low SNR.https://www.mdpi.com/1424-8220/23/10/4919underwater acoustic vector sensor arraysignal processingDOA estimationlong and short memory networktransformer |
| spellingShingle | Yangyang Xie Biao Wang Direction-of-Arrival Estimation Method Based on Neural Network with Temporal Structure for Underwater Acoustic Vector Sensor Array Sensors underwater acoustic vector sensor array signal processing DOA estimation long and short memory network transformer |
| title | Direction-of-Arrival Estimation Method Based on Neural Network with Temporal Structure for Underwater Acoustic Vector Sensor Array |
| title_full | Direction-of-Arrival Estimation Method Based on Neural Network with Temporal Structure for Underwater Acoustic Vector Sensor Array |
| title_fullStr | Direction-of-Arrival Estimation Method Based on Neural Network with Temporal Structure for Underwater Acoustic Vector Sensor Array |
| title_full_unstemmed | Direction-of-Arrival Estimation Method Based on Neural Network with Temporal Structure for Underwater Acoustic Vector Sensor Array |
| title_short | Direction-of-Arrival Estimation Method Based on Neural Network with Temporal Structure for Underwater Acoustic Vector Sensor Array |
| title_sort | direction of arrival estimation method based on neural network with temporal structure for underwater acoustic vector sensor array |
| topic | underwater acoustic vector sensor array signal processing DOA estimation long and short memory network transformer |
| url | https://www.mdpi.com/1424-8220/23/10/4919 |
| work_keys_str_mv | AT yangyangxie directionofarrivalestimationmethodbasedonneuralnetworkwithtemporalstructureforunderwateracousticvectorsensorarray AT biaowang directionofarrivalestimationmethodbasedonneuralnetworkwithtemporalstructureforunderwateracousticvectorsensorarray |