东北大学学报:自然科学版 ›› 2019, Vol. 40 ›› Issue (11): 1549-1554.DOI: 10.12068/j.issn.1005-3026.2019.11.006

• 信息与控制 • 上一篇    下一篇

基于压缩感知的水声传感网络通信方法

刘敬浩1, 董丽双1, 付晓梅1,2   

  1. (1. 天津大学 电气自动化与信息工程学院, 天津300072; 2. 天津大学 海洋科学与技术学院, 天津300072)
  • 收稿日期:2019-01-22 修回日期:2019-01-22 出版日期:2019-11-15 发布日期:2019-11-05
  • 通讯作者: 刘敬浩
  • 作者简介:刘敬浩(1963-),男,天津人,天津大学副教授; 付晓梅(1968-),女,重庆人,天津大学教授,博士生导师.
  • 基金资助:
    国家自然科学基金资助项目(61571323); 水下信息与控制重点实验室开放基金资助项目(614221801050517).

Communication Method of Underwater Acoustic Sensor Network Based on Compressed Sensing

LIU Jing-hao1, DONG Li-shuang1, FU Xiao-mei1,2   

  1. 1. School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China; 2. School of Marine Science and Technology, Tianjin University, Tianjin 300072, China.
  • Received:2019-01-22 Revised:2019-01-22 Online:2019-11-15 Published:2019-11-05
  • Contact: FU Xiao-mei
  • About author:-
  • Supported by:
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摘要: 现有研究主要针对单个传感器节点发送数据的情形,传输效率不高,而多个传感器节点同时发送数据,可以提高传输效率,但存在用户数据之间的干扰.将压缩感知应用于水声传感网络中,提出一种可实现多节点同时传输数据的大容量协作通信方法,利用传感器节点数据的稀疏特性,将多个源节点数据同时传输等效为压缩感知中的测量过程,证明了多节点并发传输过程的传输特性可以满足压缩感知中的测量矩阵的约束等距性要求.目的节点通过重构算法,可以恢复多个并发传感器节点的数据.

关键词: 水声传感网络, 水声通信, 压缩感知, 重构算法, 大容量

Abstract: Current research mainly focuses on the case that a single sensor node sends data, and the transmission efficiency is low. Using multiple sensor nodes to send data at the same time can improve the transmission efficiency, but there is interference between user’s data. Compressed sensing(CS)theory is applied to underwater acoustic sensor networks. A large-capacity cooperative communication method was proposed to realize simultaneous transmission of data by multiple nodes. Using the sparse characteristics of sensor nodes, simultaneous transmission of data from multiple source nodes is equivalent to the measurement process in CS. It is proved that the transmission characteristics of multiple nodes can satisfy the restricted isometry property(RIP)in the CS theory. The destination node can recover the data of multiple concurrent sensor nodes by the reconstruction algorithm.

Key words: underwater acoustic sensor network, underwater acoustic communication, compressed sensing(CS), reconstruction algorithm, large-capacity

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