AUV-Aided Localization for Underwater Acoustic Sensor Networks With Current Field Estimation

异步通信 水下 传播延迟 实时计算 节点(物理) 计算机科学 水声通信 水声学 声传感器 工程类 计算机网络 声学 结构工程 海洋学 物理 地质学
作者
Jing Yan,Dongbo Guo,Xiaoyuan Luo,Xinping Guan
出处
期刊:IEEE Transactions on Vehicular Technology [Institute of Electrical and Electronics Engineers]
卷期号:69 (8): 8855-8870 被引量:45
标识
DOI:10.1109/tvt.2020.2996513
摘要

Accurate sensor localization is a crucial requirement for the deployment of underwater acoustic sensor networks (UASNs) in a large variety of applications. However, the asynchronous clock, stratification effect and mobility characteristics of underwater environment make it challenging to realize accurate node localization for UASNs. This paper develops an autonomous underwater vehicle (AUV) aided localization solution for UASNs, subjected to asynchronous clock, stratification effect and mobility constraints in cyber channels. A hybrid architecture including surface buoys, AUVs, active and passive sensor nodes, is first presented to construct a cooperative location-aware network. Then, an iterative least squares estimator is developed for AUVs to capture the unknown water current parameters, through which the relationship between propagation delay and location estimation can be established. With the assistance of AUVs, two asynchronous localization algorithms are designed to estimate the locations of active and passive sensor nodes. Particularly, motion and ray compensation strategies are jointly employed to improve the localization accuracy. It is worth noticing that, the proposed localization algorithms incorporate the current field estimation into the localization process of UASNs, and more importantly, they can eliminate the influences of asynchronous clock, stratification effect and node mobility together. Moreover, performance analyses for the proposed localization solution are also presented. Finally, simulation and experimental results reveal that the node localization accuracy in this paper can be significantly improved as compared with the other works.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
研友_VZG7GZ应助坨坨西州采纳,获得10
1秒前
1秒前
华华完成签到,获得积分10
1秒前
刘明发布了新的文献求助10
1秒前
1604531786发布了新的文献求助10
3秒前
魁梧的小霸王完成签到,获得积分10
3秒前
星辰大海应助123采纳,获得10
3秒前
3秒前
是一只象完成签到,获得积分20
3秒前
科研通AI5应助海鸥海鸥采纳,获得10
4秒前
幸福遥完成签到,获得积分10
5秒前
5秒前
小王发布了新的文献求助10
5秒前
热心的代桃完成签到,获得积分10
5秒前
CodeCraft应助Olsters采纳,获得10
5秒前
6秒前
研友_IEEE快到碗里来完成签到,获得积分10
7秒前
哈哈大笑应助吴岳采纳,获得10
7秒前
7秒前
酷炫中蓝完成签到,获得积分10
7秒前
早川完成签到 ,获得积分10
8秒前
拼搏语薇完成签到,获得积分10
8秒前
科研通AI5应助SCI采纳,获得10
9秒前
dling02完成签到 ,获得积分10
9秒前
9秒前
是天使呢完成签到,获得积分10
9秒前
10秒前
10秒前
内向秋寒发布了新的文献求助10
10秒前
cc发布了新的文献求助10
10秒前
ding应助zhui采纳,获得10
11秒前
drwang120完成签到 ,获得积分10
11秒前
坨坨西州完成签到,获得积分10
12秒前
海绵体宝宝应助Louise采纳,获得20
12秒前
小马甲应助lichaoyes采纳,获得10
12秒前
12秒前
13秒前
13秒前
坨坨西州发布了新的文献求助10
14秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527849
求助须知:如何正确求助?哪些是违规求助? 3107938
关于积分的说明 9287239
捐赠科研通 2805706
什么是DOI,文献DOI怎么找? 1540033
邀请新用户注册赠送积分活动 716893
科研通“疑难数据库(出版商)”最低求助积分说明 709794