上海交通大学学报

上海交通大学学报 ›› 2022, Vol. 56 ›› Issue (12): 1658-1665.doi: 10.16183/j.cnki.jsjtu.2021.193

所属专题： 《上海交通大学学报》2022年“电子信息与电气工程”专题

• 电子信息与电气工程 • 上一篇    下一篇

自适应动态周期下的移动水声网络自定位算法

高婧洁1(), 王威1, 申晓红2   

  1. 1.长安大学 信息工程学院,西安 710064
    2.西北工业大学 航海学院,西安 710072
  • 收稿日期:2021-06-08 出版日期:2022-12-28 发布日期:2022-10-14
  • 作者简介:高婧洁(1988-),女,陕西省西安市人,讲师,主要从事水声网络通信与定位研究;E-mail:gaojingj@chd.edu.cn.
  • 基金资助:
    国家自然科学基金(61901057);国家自然科学基金(61871059)

A Self-Localization Algorithm with Adaptive and Dynamic Observation Period for Mobile Underwater Acoustic Networks

GAO Jingjie1(), WANG Wei1, SHEN Xiaohong2   

  1. 1. School of Information Engineering, Chang’an University, Xi’an 710064, China
    2. School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
  • Received:2021-06-08 Online:2022-12-28 Published:2022-10-14

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摘要：

针对移动水声网络中定位精度与通信开销之间的矛盾,提出一种自适应动态周期下的移动水声网络自定位算法.该算法根据系统状态估计与观测采样之间的残差,设计自适应的动态周期选择机制和非均匀的动态更新网络定位周期,进而实现非均匀动态周期下的移动水声网络高精度预测定位.该算法无需大量通信观测即可实现移动节点位置的实时跟踪,达到了定位精度与通信开销间的平衡.仿真结果表明,所提算法既保证了网络的定位估计精度,又减小了冗余定位通信开销,实现了有限通信开销下的高精度定位,更适用于精度要求高且通信带宽有限的水下环境中.

关键词: 移动水声网络, 自定位, 自适应动态周期

Abstract:

In order to resolve the conflicts between the communication traffic and the localization accuracy, a self-localization algorithm with adaptive and dynamic observation period for mobile underwater acoustic networks (MUANs) was proposed to improve the localization performance. First, an adaptive and dynamic observation period selection scheme was designed, which could generate a non-uniform observation period vector according to the residual change. Then, based on the non-uniform observation period vector, a self-localization algorithm was proposed, which could precisely predict the trajectory of each mobile node in the network. The simulation results show that the proposed algorithm, which could balance the tradeoff between the localization accuracy and the communication cost, is more suitable for the underwater environment.

Key words: mobile underwater acoustic networks (MUANs), self-localization, adaptive and dynamic observation period

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