{"title":"基于深度学习的水声矢量传感器阵列数据驱动DOA估计方法","authors":"Yangyang Xie, Biao Wang","doi":"10.4031/mtsj.57.3.3","DOIUrl":null,"url":null,"abstract":"Abstract Direction of arrival (DOA) estimation is a fundamental problem in underwater acoustic vector sensor array signal processing. Because of the advantages of deep learning technology, this paper proposes two categories of data-driven DOA estimation methods for underwater acoustic vector sensor array, which transform the DOA estimation problem into a neural network classification problem. Specifically, one is the DOA estimation method of convolutional neural network based on teacher-student noise reduction (TS-CNN), which considers the covariance matrix as the training data set; the other is a DOA estimation method based on long-short term memory network and attention mechanism (LSTM-ATT), which applies the time-domain signal as the training data set. The experimental simulation results show that: 1) when the number of array elements is small, the accuracy of the DOA estimation method based on TS-CNN is equivalent to that of traditional methods, and it can effectively suppress the influence of noise when the signal-to-noise ratio (SNR) is low; 2) the accuracy of DOA estimation method based on LSTM-ATT is much higher than that of traditional Multiple Signal Classification method, especially in the case of low SNR, which also proves the importance of temporal characteristics for DOA estimation in a real environment.","PeriodicalId":49878,"journal":{"name":"Marine Technology Society Journal","volume":"16 1","pages":"0"},"PeriodicalIF":0.7000,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Data-Driven DOA Estimation Methods Based on Deep Learning for Underwater Acoustic Vector Sensor Array\",\"authors\":\"Yangyang Xie, Biao Wang\",\"doi\":\"10.4031/mtsj.57.3.3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Direction of arrival (DOA) estimation is a fundamental problem in underwater acoustic vector sensor array signal processing. Because of the advantages of deep learning technology, this paper proposes two categories of data-driven DOA estimation methods for underwater acoustic vector sensor array, which transform the DOA estimation problem into a neural network classification problem. Specifically, one is the DOA estimation method of convolutional neural network based on teacher-student noise reduction (TS-CNN), which considers the covariance matrix as the training data set; the other is a DOA estimation method based on long-short term memory network and attention mechanism (LSTM-ATT), which applies the time-domain signal as the training data set. The experimental simulation results show that: 1) when the number of array elements is small, the accuracy of the DOA estimation method based on TS-CNN is equivalent to that of traditional methods, and it can effectively suppress the influence of noise when the signal-to-noise ratio (SNR) is low; 2) the accuracy of DOA estimation method based on LSTM-ATT is much higher than that of traditional Multiple Signal Classification method, especially in the case of low SNR, which also proves the importance of temporal characteristics for DOA estimation in a real environment.\",\"PeriodicalId\":49878,\"journal\":{\"name\":\"Marine Technology Society Journal\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2023-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Marine Technology Society Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4031/mtsj.57.3.3\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, OCEAN\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Marine Technology Society Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4031/mtsj.57.3.3","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, OCEAN","Score":null,"Total":0}
Data-Driven DOA Estimation Methods Based on Deep Learning for Underwater Acoustic Vector Sensor Array
Abstract Direction of arrival (DOA) estimation is a fundamental problem in underwater acoustic vector sensor array signal processing. Because of the advantages of deep learning technology, this paper proposes two categories of data-driven DOA estimation methods for underwater acoustic vector sensor array, which transform the DOA estimation problem into a neural network classification problem. Specifically, one is the DOA estimation method of convolutional neural network based on teacher-student noise reduction (TS-CNN), which considers the covariance matrix as the training data set; the other is a DOA estimation method based on long-short term memory network and attention mechanism (LSTM-ATT), which applies the time-domain signal as the training data set. The experimental simulation results show that: 1) when the number of array elements is small, the accuracy of the DOA estimation method based on TS-CNN is equivalent to that of traditional methods, and it can effectively suppress the influence of noise when the signal-to-noise ratio (SNR) is low; 2) the accuracy of DOA estimation method based on LSTM-ATT is much higher than that of traditional Multiple Signal Classification method, especially in the case of low SNR, which also proves the importance of temporal characteristics for DOA estimation in a real environment.
期刊介绍:
The Marine Technology Society Journal is the flagship publication of the Marine Technology Society. It publishes the highest caliber, peer-reviewed papers, six times a year, on subjects of interest to the society: marine technology, ocean science, marine policy, and education.