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  • 标题:Mobile target localization based on iterative tracing for underwater wireless sensor networks
  • 本地全文:下载
  • 作者:Ruolin Guo ; Danyang Qin ; Min Zhao
  • 期刊名称:International Journal of Distributed Sensor Networks
  • 印刷版ISSN:1550-1329
  • 电子版ISSN:1550-1477
  • 出版年度:2020
  • 卷号:16
  • 期号:7
  • 页码:1
  • DOI:10.1177/1550147720940634
  • 出版社:Hindawi Publishing Corporation
  • 摘要:In underwater wireless sensor networks, sensor position information has important value in network protocols and collaborative detection. However, many challenges were introduced in positioning sensor nodes due to the complexity of the underwater environment. Aiming at the problem of the stratification effect of underwater acoustic waves, the long propagation delay of messages, as well as the mobility of sensor nodes, a mobile target localization scheme for underwater wireless sensor network is proposed based on iterative tracing. Four modules are established in the mobile target localization based on iterative tracing: the data collection and rough position estimation, the estimation and compensation of propagation delay, the node localization, and the iteration. The deviation of distance estimation due to the assumption that acoustic waves propagate along straight lines in an underwater environment is compensated by the mobile target localization based on iterative tracing, and weighted least squares estimation method is used to perform linear regression. Moreover, an interacting multiple model algorithm is put forward to reduce the positioning error caused by the mobility of sensor nodes, and the two services of node time synchronization and localization assist each other during the iteration to improve the accuracy of both parties. The simulation results show that the proposed scheme can achieve higher localization accuracy than the similar schemes, and the positioning errors caused by the above three problems can be reduced effectively.
  • 关键词:Underwater wireless sensor networks; stratification effect; propagation delay; interacting multiple model
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