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Frontiers of Information Technology & Electronic Engineering

ISSN 2095-9184 (print), ISSN 2095-9230 (online)

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

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.

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

Chinese Summary  <21> 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。

关键词组:目标跟踪;加权融合;互信息;节点选择;水下无线传感器网络


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DOI:

10.1631/FITEE.1601695

CLC number:

TN92

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On-line Access:

2018-06-07

Received:

2016-11-09

Revision Accepted:

2017-03-14

Crosschecked:

2018-04-03

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