Full Text:   <603>

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CLC number: TN92

On-line Access: 2018-06-07

Received: 2016-11-09

Revision Accepted: 2017-03-14

Crosschecked: 2018-04-03

Cited: 0

Clicked: 2047

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Mei-qin Liu

http://orcid.org/0000-0003-0693-6574

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Frontiers of Information Technology & Electronic Engineering  2018 Vol.19 No.4 P.544-556

http://doi.org/10.1631/FITEE.1601695


Mutual-information based weighted fusion for target tracking in underwater wireless sensor networks


Author(s):  Duo Zhang, Mei-qin Liu, Sen-lin Zhang, Zhen Fan, Qun-fei Zhang

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

Corresponding email(s):   zhangduo@zju.edu.cn, liumeiqin@zju.edu.cn, slzhang@zju.edu.cn, fanzhen@zju.edu.cn, zhangqf@nwpu.edu.cn

Key Words:  Target tacking, Fusion weight, Mutual information, Node selection, Underwater wireless sensor networks


Duo Zhang, Mei-qin Liu, Sen-lin Zhang, Zhen Fan, Qun-fei Zhang. Mutual-information based weighted fusion for target tracking in underwater wireless sensor networks[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(4): 544-556.

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publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601695"
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T1 - Mutual-information based weighted fusion for target tracking in underwater wireless sensor networks
A1 - Duo Zhang
A1 - Mei-qin Liu
A1 - Sen-lin Zhang
A1 - Zhen Fan
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Abstract: 
underwater wireless sensor networks (UWSNs) can provide a promising solution to underwater target tracking. Due to limited energy and bandwidth resources, only a small number of nodes are selected to track a target at each interval. Because all measurements are fused together to provide information in a fusion center, fusion weights of all selected nodes may affect the performance of target tracking. As far as we know, almost all existing tracking schemes neglect this problem. We study a weighted fusion scheme for target tracking in UWSNs. First, because the mutual information (MI) between a node's measurement and the target state can quantify target information provided by the node, it is calculated to determine proper fusion weights. Second, we design a novel multi-sensor weighted particle filter (MSWPF) using fusion weights determined by MI. Third, we present a local node selection scheme based on posterior Cramer-Rao lower bound (PCRLB) to improve tracking efficiency. Finally, simulation results are presented to verify the performance improvement of our scheme with proper fusion weights.

基于互信息的水下无线传感器网络目标跟踪与加权融合

摘要:水下无线传感器网络为水下目标跟踪问题提供了可靠有效支持,但水下网络能量和带宽资源有限,只能选择一部分节点参与跟踪任务。融合中心通过收集、融合各个传感器发送的量测进行目标跟踪,因此设计更好的融合权值极为重要。针对水下目标跟踪中的加权融合问题,首先通过计算量测与目标状态之间的互信息,利用互信息衡量融合权重;其次利用互信息融合权重设计一种新的多传感器加权粒子滤波算法,利用克拉美罗(Cramer-Rao)下界设计节点选择方案,以提高跟踪算法效率;最后通过仿真实验对算法进行验证。仿真结果表明,通过选择合适融合权值,目标状态估计精度显著提高。

关键词:目标跟踪;加权融合;互信息;节点选择;水下无线传感器网络

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

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