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[1]徐魏超,王冠凌,陈孟元.无人机协助下基于SR-CKF的无线传感器网络节点定位研究[J].智能系统学报,2019,14(03):575-581.[doi:10.11992/tis.201709019]
 XU Weichao,WANG Guanling,CHEN Mengyuan.Node localization of wireless sensor networks based on SR-CKF assisted by unmanned aerial vehicles[J].CAAI Transactions on Intelligent Systems,2019,14(03):575-581.[doi:10.11992/tis.201709019]
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无人机协助下基于SR-CKF的无线传感器网络节点定位研究(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年03期
页码:
575-581
栏目:
出版日期:
2019-05-05

文章信息/Info

Title:
Node localization of wireless sensor networks based on SR-CKF assisted by unmanned aerial vehicles
作者:
徐魏超 王冠凌 陈孟元
安徽工程大学 安徽省电气传动与控制重点实验室, 安徽 芜湖 241000
Author(s):
XU Weichao WANG Guanling CHEN Mengyuan
Anhui Key Laboratory of Electric Drive and Control, Anhui Polytechnic University, Wuhu 241000, China
关键词:
无人机无线传感器网络节点极大似然阀值选择协作定位平方根容积卡尔曼算法
Keywords:
unmanned aerial vehiclewireless sensor network nodemaximum likelihood estimation methodthreshold selectioncollaboration localizationsquare root volume kalman algorithm
分类号:
TN92;TP393
DOI:
10.11992/tis.201709019
摘要:
针对无线传感器网络(WSN)节点的实际应用场合大多数分布在复杂的三维地形,并且当无线传感器网络分布规模达到一定程度时,对每一个传感器节点装载GPS模块来实现节点定位不切实际的情况,提出了一种无人机(UAV)协助下利用极大似然估计法(MLE)对未知节点进行初步定位,引入平方根容积卡尔曼滤波(SR-CKF)算法对未知节点进行精确定位,采用阈值选择的更新策略来减小非线性因素的影响。仿真结果表明:所提出的UAV-WSN-MLE-SRCKF协作定位方式实现了三维地形中未知传感器节点的定位估计,大量减少了装载GPS模块所带来的成本,同时也提高了定位精度和稳定性。
Abstract:
Most applications of wireless sensor network nodes are distributed in the complex 3D terrain. When the wireless sensor network distribution scale reaches a certain extent, realizing the node positioning by loading the global positioning system (GPS) module on every sensor node becomes impractical. In view of this situation, this paper puts forward a kind of unmanned aerial vehicle (UAV)-assisted maximum likelihood estimation (MLE) method for the preliminary positioning of unknown nodes. We introduce the square root cubature Kalman filtering (SRCKF) algorithm for the precise positioning of unknown nodes and use the threshold selection update strategy to reduce the influence of nonlinear factors. The simulation results show that the UAV-WSN-MLE-SRCKF collaboration localization method proposed in this paper realizes the location estimation of unknown sensor nodes in the 3D terrain, reduces the cost of loading GPS modules to a large extent, and simultaneously improves the positioning accuracy and stability.

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备注/Memo

备注/Memo:
收稿日期:2017-09-08。
基金项目:安徽省高校优秀青年人才支持计划项目(gxyqZD2018050).
作者简介:徐魏超,男,1993年生,硕士研究生,主要研究方向为无人机、无线传感器网络、智能信息处理等;王冠凌,男,1971年生,教授,研究生导师,主要研究方向为检测自动化装置、嵌入式系统开发等。主持省教育厅教研重点、重大项目2项以及省教育厅科研重点项目1项。发表学术论文20余篇。授权国家发明专利4项;陈孟元,男,1984年生,副教授,主要研究方向为嵌入式系统开发、图像处理、传感器信息融合及优化。主持安徽省高等学校自然科学研究项目等10余项,发表学术论文30余篇,授权国家发明专利4项。
通讯作者:王冠凌.E-mail:agc3001@163.com
更新日期/Last Update: 1900-01-01