Estimation of Doubly Spread Underwater Acoustic Channel via Gram-Schmidt Matching Pursuit

Feiyun Wu, Kunde Yang, Tian Tian, Chunlong Huang, Yunchao Zhu, F. Tong
{"title":"Estimation of Doubly Spread Underwater Acoustic Channel via Gram-Schmidt Matching Pursuit","authors":"Feiyun Wu, Kunde Yang, Tian Tian, Chunlong Huang, Yunchao Zhu, F. Tong","doi":"10.1109/OCEANSE.2019.8867540","DOIUrl":null,"url":null,"abstract":"The underwater acoustic channel (UAC) exhibits strongly time delay and Doppler (DD) spread especially when the UAC is rapidly time-varying. These dynamic factors result to a serious impact on communication performance such as Inter-Symbol Interference (ISI). Hence, estimation of complex amplitude, time delay and the Dopplers of the UAC becomes the key part in underwater acoustic communication and is hopeful for improving the performance of equalization. However, the estimation is challenged by multiple factors to be estimated in delay and Doppler dimensions. This study exploits the sparsity of the UAC and develops an estimator via using Gram-Schmidt to find orthogonal bases, which leads to the fast and orthogonal way to select the supports of the dictionaries. The support list of the dictionaries constructed by probe signal can be used for estimating the DD functions from a noisy received signal. Matching Pursuit (MP) and Least Square (LS) methods are used for comparisons. The effectiveness of the proposed method is verified by the experimental data.","PeriodicalId":375793,"journal":{"name":"OCEANS 2019 - Marseille","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"OCEANS 2019 - Marseille","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/OCEANSE.2019.8867540","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

The underwater acoustic channel (UAC) exhibits strongly time delay and Doppler (DD) spread especially when the UAC is rapidly time-varying. These dynamic factors result to a serious impact on communication performance such as Inter-Symbol Interference (ISI). Hence, estimation of complex amplitude, time delay and the Dopplers of the UAC becomes the key part in underwater acoustic communication and is hopeful for improving the performance of equalization. However, the estimation is challenged by multiple factors to be estimated in delay and Doppler dimensions. This study exploits the sparsity of the UAC and develops an estimator via using Gram-Schmidt to find orthogonal bases, which leads to the fast and orthogonal way to select the supports of the dictionaries. The support list of the dictionaries constructed by probe signal can be used for estimating the DD functions from a noisy received signal. Matching Pursuit (MP) and Least Square (LS) methods are used for comparisons. The effectiveness of the proposed method is verified by the experimental data.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于Gram-Schmidt匹配跟踪的双扩频水声信道估计
水声信道表现出强烈的时间延迟和多普勒(DD)扩散,特别是在快速时变的水声信道中。这些动态因素会对通信性能造成严重影响,如码间干扰(ISI)。因此,对UAC的复幅度、时延和多普勒的估计成为水声通信的关键部分,有望提高均衡性能。然而,在时延和多普勒维度上,估计受到多种因素的挑战。本研究利用UAC的稀疏性,利用Gram-Schmidt寻找正交基的方法开发了一个估计器,从而实现了快速正交选择字典支持的方法。探测信号构造的字典支持列表可用于估计接收到的带噪声信号的DD函数。采用匹配追踪(MP)和最小二乘(LS)方法进行比较。实验数据验证了该方法的有效性。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A Data-driven Vessel Motion Model for Offshore Access Forecasting Decentralized System Intelligence in Data Driven Networks for Shipping Industrial Applications: Digital Models to Blockchain Technologies Robust 3D Shape Classification Method using Simulated Multi View Sonar Images and Convolutional Nueral Network Weighted Grid Partitioning for Panel-Based Bathymetric SLAM Fishing Spot Detection Using Sea Water Temperature Pattern by Nonlinear Clustering
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1