舰船电子工程2024，Vol.44Issue(1)：196-199,229,5.DOI:10.3969/j.issn.1672-9730.2024.01.038
基于多任务神经网络的水下震源定位方法研究
Research on Underwater Source Localization Method Based on Multi-task Neural Networks
摘要
Abstract
In order to solve the problems of large sensor array,difficult deployment and environmental mismatch of traditional underwater passive positioning algorithm,a method of underwater source localization based on multi-task neural network is pro-posed.By simulating the shallow sea acoustic field dataset,using the relative time difference of the acoustic signal to reach each measurement sensor,combined with the deep learning method,the MTL-Attention-UNet neural network model is designed on the basis of the multi-task convolutional neural network MTL-CNN(Multi-task Convolutional Neural Network)and Attention-UNet structure,and the distance and depth of the underwater seismic source are jointly estimated.The simulation results show that the av-erage absolute error of positioning the underwater source by MTL-Attention-UNet model is smaller than that of the MTL-CNN net-work model,and the positioning performance is better.关键词
震源定位/神经网络/平均绝对误差Key words
source localization/neural networks/average absolute error分类
信息技术与安全科学引用本文复制引用
杨丽燕,王黎明,韩星程,武国强,王鸿儒,马文..基于多任务神经网络的水下震源定位方法研究[J].舰船电子工程,2024,44(1):196-199,229,5.基金项目
国家自然科学青年基金项目(编号:62203405) （编号:62203405）
山西省重点研发计划(编号:2022ZDYF079) （编号:2022ZDYF079）
山西省应用基础研究计划(编号:20210302124545)资助. （编号:20210302124545）
