Barycentric Coordinate-Based Distributed Localization Over 3-D Underwater Wireless Sensor Networks

IF 3 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-08-08 DOI:10.1109/TSIPN.2024.3440644

Lei Shi;Shaojie Yao;Nianwen Ning;Yi Zhou

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引用次数: 0

Abstract

Accurate localization of underwater wireless sensor networks (UWSNs) are essential for their seamless integration and operational efficacy in marine environments, yet it poses a considerable technical challenge due to the distinctive limitations of underwater communications. This paper addresses the intricate 3-D localization problem for UWSNs by proposing an innovative method based on barycentric coordinates and relative distance measurements. In order to adapt to the influence of underwater communication constraints, a barycentric coordinate-based distributed iterative localization method combining with the processing of underwater background noise is proposed. It is proved theoretically that the proposed method can almost guarantee the convergence to the exact location of each underwater sensor node. Finally, the effectiveness of the proposed localization method is verified by numerical simulations. The proposed localization scheme requires only small number of anchor nodes, thus facilitating the development of broader and more cost-effective underwater localization systems.

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基于重心坐标的三维水下无线传感器网络分布式定位

水下无线传感器网络（UWSN）的精确定位对其在海洋环境中的无缝集成和运行效率至关重要，但由于水下通信的独特局限性，这构成了相当大的技术挑战。本文针对 UWSN 错综复杂的三维定位问题，提出了一种基于偏心坐标和相对距离测量的创新方法。为了适应水下通信限制的影响，本文提出了一种基于重心坐标的分布式迭代定位方法，并结合了对水下背景噪声的处理。理论证明，所提出的方法几乎可以保证收敛到每个水下传感器节点的精确位置。最后，通过数值模拟验证了所提定位方法的有效性。所提出的定位方案只需要少量的锚节点，因此有利于开发更广泛、更具成本效益的水下定位系统。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Signal and Information Processing over Networks Computer Science-Computer Networks and Communications

CiteScore

5.80

自引率

12.50%

发文量

期刊介绍： The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.