Full Text:   <2185>

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CLC number: TP274

On-line Access: 2018-10-05

Received: 2017-09-11

Revision Accepted: 2018-04-17

Crosschecked: 2018-08-15

Cited: 0

Clicked: 6337

Citations:  Bibtex RefMan EndNote GB/T7714


Mei-qin Liu


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Frontiers of Information Technology & Electronic Engineering  2018 Vol.19 No.8 P.999-1012


Energy-efficient localization and target tracking via underwater mobile sensor networks

Author(s):  Hua-yan Chen, Mei-qin Liu, Sen-lin Zhang

Affiliation(s):  State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   chenhuayan@zju.edu.cn, liumeiqin@zju.edu.cn, slzhang@zju.edu.cn

Key Words:  Underwater mobile sensor networks, Energy-efficient, Sensor localization, Target tracking

Hua-yan Chen, Mei-qin Liu, Sen-lin Zhang. Energy-efficient localization and target tracking via underwater mobile sensor networks[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(8): 999-1012.

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T1 - Energy-efficient localization and target tracking via underwater mobile sensor networks
A1 - Hua-yan Chen
A1 - Mei-qin Liu
A1 - Sen-lin Zhang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 19
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1700598

underwater mobile sensor networks (UMSNs) with free-floating sensors are more suitable for understanding the immense underwater environment. target tracking, whose performance depends on sensor localization accuracy, is one of the broad applications of UMSNs. However, in UMSNs, sensors move with environmental forces, so their positions change continuously, which poses a challenge on the accuracy of sensor localization and target tracking. We propose a high-accuracy localization with mobility prediction (HLMP) algorithm to acquire relatively accurate sensor location estimates. The HLMP algorithm exploits sensor mobility characteristics and the multi-step Levinson-Durbin algorithm to predict future positions. Furthermore, we present a simultaneous localization and target tracking (SLAT) algorithm to update sensor locations based on measurements during the process of target tracking. Simulation results demonstrate that the HLMP algorithm can improve localization accuracy significantly with low energy consumption and that the SLAT algorithm can further decrease the sensor localization error. In addition, results prove that a better localization accuracy will synchronously improve the target tracking performance.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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