An Underwater Velocity-Independent DOA Estimation Method Based on Cascaded Neural Network

IF 1.8 3区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Sihan Yuan, Gengxin Ning, Yushen Lin
{"title":"An Underwater Velocity-Independent DOA Estimation Method Based on Cascaded Neural Network","authors":"Sihan Yuan, Gengxin Ning, Yushen Lin","doi":"10.1007/s00034-024-02838-4","DOIUrl":null,"url":null,"abstract":"<p>The underwater environment introduces uncertainty into the acoustic velocity, which affects the performance of traditional direction of arrival (DOA) estimation methods. This research proposes a cascaded neural network based underwater DOA estimate approach to address this issue. In this method, the cascade neural network is composed of a velocity regressor and a velocity classifier. To determine the estimated value of acoustic velocity, the velocity classifier first breaks down the input data into distinct velocity domains. It then regulates the velocity regression process. Then, the array steering matrix predicted by the blind source separation algorithm is utilized to determine the angle, and the acoustic velocity is modiffed by the cascaded neural network. Eventually, it is possible to derive the DOA estimation value under the calculated acoustic velocity. The suggested method has a high estimation accuracy especially when the acousitc velocity is unknown, as shown by the simulation results.</p>","PeriodicalId":10227,"journal":{"name":"Circuits, Systems and Signal Processing","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Circuits, Systems and Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s00034-024-02838-4","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

Abstract

The underwater environment introduces uncertainty into the acoustic velocity, which affects the performance of traditional direction of arrival (DOA) estimation methods. This research proposes a cascaded neural network based underwater DOA estimate approach to address this issue. In this method, the cascade neural network is composed of a velocity regressor and a velocity classifier. To determine the estimated value of acoustic velocity, the velocity classifier first breaks down the input data into distinct velocity domains. It then regulates the velocity regression process. Then, the array steering matrix predicted by the blind source separation algorithm is utilized to determine the angle, and the acoustic velocity is modiffed by the cascaded neural network. Eventually, it is possible to derive the DOA estimation value under the calculated acoustic velocity. The suggested method has a high estimation accuracy especially when the acousitc velocity is unknown, as shown by the simulation results.

Abstract Image

基于级联神经网络的与速度无关的水下 DOA 估算方法
水下环境会给声速带来不确定性,从而影响传统的到达方向(DOA)估计方法的性能。针对这一问题,本研究提出了一种基于级联神经网络的水下 DOA 估计方法。在该方法中,级联神经网络由速度回归器和速度分类器组成。为了确定声速的估计值,速度分类器首先将输入数据分解成不同的速度域。然后,它对速度回归过程进行调节。然后,利用盲源分离算法预测的阵列转向矩阵来确定角度,并通过级联神经网络对声速进行调制。最终,可以在计算出的声速下得出 DOA 估计值。模拟结果表明,所建议的方法具有很高的估计精度,尤其是在声速未知的情况下。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Circuits, Systems and Signal Processing
Circuits, Systems and Signal Processing 工程技术-工程:电子与电气
CiteScore
4.80
自引率
13.00%
发文量
321
审稿时长
4.6 months
期刊介绍: Rapid developments in the analog and digital processing of signals for communication, control, and computer systems have made the theory of electrical circuits and signal processing a burgeoning area of research and design. The aim of Circuits, Systems, and Signal Processing (CSSP) is to help meet the needs of outlets for significant research papers and state-of-the-art review articles in the area. The scope of the journal is broad, ranging from mathematical foundations to practical engineering design. It encompasses, but is not limited to, such topics as linear and nonlinear networks, distributed circuits and systems, multi-dimensional signals and systems, analog filters and signal processing, digital filters and signal processing, statistical signal processing, multimedia, computer aided design, graph theory, neural systems, communication circuits and systems, and VLSI signal processing. The Editorial Board is international, and papers are welcome from throughout the world. The journal is devoted primarily to research papers, but survey, expository, and tutorial papers are also published. Circuits, Systems, and Signal Processing (CSSP) is published twelve times annually.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信