无人机协助下基于SR-CKF的无线传感器网络节点定位研究
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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(SRCKF)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。
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.
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.
引文
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[14]CHAURASIYA V K,JAIN N,NANDI G C.A novel distance estimation approach for 3D localization in wireless sensor network using multi dimensional scaling[J].Information fusion,2014,15:5-18.
[15]YANG Xi,YAN Fang,LIU Jun.3D localization algorithm based on voronoi diagram and rank sequence in wireless sensor network[J].Scientific programming,2017,2017:4769710.
[16]KUMAR A,KHOSLA A,SAINI J S,et al.Range-free3D node localization in anisotropic wireless sensor networks[J].Applied soft computing,2015,34:438-448.
[17]余木琪,邓平.一种基于CKF的无线传感器网络分布式定位算法[J].传感技术学报,2015,28(7):1041-1045.YU Muqi,DENG Ping.A distributed localization algorithm based on cubature Kalman filter in wireless sensor networks[J].Chinese journal of sensors and actuators,2015,28(7):1041-1045.
[18]孙尧.基于无人机的无线传感器网络节点定位研究[D].沈阳:东北大学,2013.SUN Yao.Study on node localization in wireless sensor network based on unmanned aerial vehicle[D].Shenyang:Northeastern University,2013.
[19]郝燕玲,杨峻巍,陈亮,等.基于SRCKF的水下航行器动基座初始对准技术[J].华中科技大学学报(自然科学版),2012,40(2):123-127.HAO Yanling,YANG Junwei,CHEN Liang,et al.Initial alignment method of the dynamic base for underwater vehicles using SRCKF[J].Journal of Huazhong University of Science and Technology(natural science edition),2012,40(2):123-127.
[2]袁浩.一种基于GPS技术的新型无线传感网络节点设计[J].自动化与信息工程,2009,30(1):46-48.YUAN Hao.A new type wireless sensor networks node design base on GPS technology[J].Automation&information engineering,2009,30(1):46-48.
[3]梅举,陈涤,辛玲.基于蒙特卡洛方法的移动传感网节点定位优化算法[J].传感技术学报,2013,26(5):689-694.MEI Ju,CHEN Di,XIN Ling.Optimized localization scheme for mobile wireless sensor networks based on monte Carlo method[J].Chinese journal of sensors and actuators,2013,26(5):689-694.
[4]夏少波,邹建梅,朱晓丽,等.无线传感器网络DV-Hop定位算法的改进[J].计算机应用,2015,35(2):340-344.XIA Shaobo,ZOU Jianmei,ZHU Xiaoli,et al.Improvement on DV-Hop localization algorithm in wireless sensor networks[J].Journal of computer applications,2015,35(2):340-344.
[5]王小平,罗军,沈昌祥.三边测量法的结果稳定性研究[J].计算机工程与科学,2012,34(6):12-17.WANG Xiaoping,LUO Jun,SHEN Changxiang.Research on the stability of trilateration algorithms[J].Computer engineering&science,2012,34(6):12-17.
[6]胡中栋,谢金伟.基于山区地形的无线传感器网络三维定位机制[J].传感技术学报,2015,28(3):408-411.HU Zhongdong,XIE Jinwei.The 3D Localization mechanism for wireless sensor networks based on mountainous terrain[J].Chinese journal of sensors and actuators,2015,28(3):408-411.
[7]李彬,吕晓军,贾新春,等.无线传感器网络三维节点的插值规划定位[J].北京邮电大学学报,2016,39(5):94-98.LI Bin,LüXiaojun,JIA Xinchun,et al.Interpolation and0-1 programming based 3D localization algorithm in wireless sensor networks[J].Journal of Beijing University of Posts and Telecommunications,2016,39(5):94-98.
[8]王俊,李树强,刘刚.无线传感器网络三维定位交叉粒子群算法[J].农业机械学报,2014,45(5):233-238.WANG Jun,LI Shuqiang,LIU Gang.Three-dimensional localization method of agriculture wireless sensor networks based on crossover particle swarm optimization[J].Transactions of the Chinese society for agricultural machinery,2014,45(5):233-238.
[9]崔鸿飞,刘佳,顾晶晶,等.基于局部保持典型相关分析的无线传感器网络三维定位算法[J].计算机科学,2017,44(9):105-109,130.CUI Hongfei,LIU Jia,GU Jingjing,et al.3D localization estimation algorithm based on locality preserving canonical correlation analysis in wireless sensor networks[J].Computer science,2017,44(9):105-109,130.
[10]KIM E,KIM K.Distance estimation with weighted least squares for mobile beacon-based localization in wireless sensor networks[J].IEEE signal processing letters,2010,17(6):559-562.
[11]SHAREEF A,ZHU Yifeng,MUSAVI M.Localization using neural networks in wireless sensor networks[C]//Proceedings of the 1stInternational Conference on Mobile Wireless Middleware,Operating Systems,and Applications.Innsbruck,Austria,2008:1-7.
[12]ZHANG Liqiang,CHENG Qiang,WANG Yingge,et al.A novel distributed sensor positioning system using the dual of target tracking[J].IEEE transactions on computers,2008,57(2):246-260.
[13]XU Yan,ZHUANG Yi,GU Jingjing.An improved 3D localization algorithm for the wireless sensor network[J].International journal of distributed sensor networks,2015,2015:98.
[14]CHAURASIYA V K,JAIN N,NANDI G C.A novel distance estimation approach for 3D localization in wireless sensor network using multi dimensional scaling[J].Information fusion,2014,15:5-18.
[15]YANG Xi,YAN Fang,LIU Jun.3D localization algorithm based on voronoi diagram and rank sequence in wireless sensor network[J].Scientific programming,2017,2017:4769710.
[16]KUMAR A,KHOSLA A,SAINI J S,et al.Range-free3D node localization in anisotropic wireless sensor networks[J].Applied soft computing,2015,34:438-448.
[17]余木琪,邓平.一种基于CKF的无线传感器网络分布式定位算法[J].传感技术学报,2015,28(7):1041-1045.YU Muqi,DENG Ping.A distributed localization algorithm based on cubature Kalman filter in wireless sensor networks[J].Chinese journal of sensors and actuators,2015,28(7):1041-1045.
[18]孙尧.基于无人机的无线传感器网络节点定位研究[D].沈阳:东北大学,2013.SUN Yao.Study on node localization in wireless sensor network based on unmanned aerial vehicle[D].Shenyang:Northeastern University,2013.
[19]郝燕玲,杨峻巍,陈亮,等.基于SRCKF的水下航行器动基座初始对准技术[J].华中科技大学学报(自然科学版),2012,40(2):123-127.HAO Yanling,YANG Junwei,CHEN Liang,et al.Initial alignment method of the dynamic base for underwater vehicles using SRCKF[J].Journal of Huazhong University of Science and Technology(natural science edition),2012,40(2):123-127.
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