[1]李刚,陈俊杰.一种基于测距的蒙特卡罗盒定位算法[J].东南大学学报(自然科学版),2012,42(6):1105-1110.[doi:10.3969/j.issn.1001-0505.2012.06.016]
 Li Gang,Chen Junjie.Range-based Monte Carlo localization boxed algorithm[J].Journal of Southeast University (Natural Science Edition),2012,42(6):1105-1110.[doi:10.3969/j.issn.1001-0505.2012.06.016]
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一种基于测距的蒙特卡罗盒定位算法()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
42
期数:
2012年第6期
页码:
1105-1110
栏目:
计算机科学与工程
出版日期:
2012-11-20

文章信息/Info

Title:
Range-based Monte Carlo localization boxed algorithm
作者:
李刚1 陈俊杰12
1 东南大学仪器科学与工程学院,南京 210096; 2 东南大学常州研究院,常州213164
Author(s):
Li Gang1 Chen Junjie12
1 School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
2 Changzhou Academe, Southeast University, Changzhou 213164, China
关键词:
移动定位 RSSI 自适应采样 加权计算
Keywords:
mobile localization received signal strength indicator(RSSI) adaptive sampling weighted calculation
分类号:
TP393
DOI:
10.3969/j.issn.1001-0505.2012.06.016
摘要:
针对以蒙特卡罗算法为基础的几种无线传感器网络移动节点定位算法普遍存在定位精度和采样效率低的问题,提出一种基于测距的蒙特卡罗盒定位算法(RBMCB).通过测距信息构建更精确的采样盒,并采用KLD采样方法自适应确定最大采样次数,然后在采样盒内采样,最后通过测距信息对样本进行权值计算并将所有样本的加权平均值作为位置估计.仿真实验表明:RBMCB算法相比MCB算法定位精度提高了30%,相比Range-Based MCL算法定位精度提高了10%,并具有更高的采样效率.因此,RBMCB算法适用于对定位精度和采样效率要求高的场合.
Abstract:
Some common problems, such as low location accuracy and low sampling efficiency, are unavoidable in current node localization algorithms based on Monte Carlo localization(MCL)in mobile wireless sensor networks. To improve these issues, a range-based MCL boxed algorithm(RBMCB)is proposed. In the algorithm, a precise sample box is constructed through the range information. Meanwhile, the maximum sampling times is adaptively determined by Kullback-Leibler distance(KLD)sampling and weighted calculation is used for analyzing the sample range information. Finally, the weighted mean value of all samples is taken as the location estimation. Simulation results show that the proposed algorithm can enhance the location accuracy by 30% comparing to the MCB algorithm, and 10% comparing to the range-based MCL algorithm. Furthermore, the results also show that the proposed algorithm can achieve higher sampling efficiency. Thus, RBMCB can be applied to the circumstance where the high location accuracy and sampling efficiency are required.

参考文献/References:

[1] Ou C-H.A localization scheme for wireless sensor networks using mobile anchors with directional antennas[J].IEEE Sensors Journal,2011,7(11):1607-1616.
[2] Xiao Qingjun,Xiao Bin,Wang Jianping.Multihop range-free localization in anisotropic wireless sensor networks:a pattern-driven scheme[J].IEEE Transactions on Mobile Computing,2010,9(11):1592-1607.
[3] Zhang Shigeng,Cao Jiannong,Chen Lijun,et al.Accurate and energy-efficient range-free localization for mobile sensor networks[J].IEEE Transactions on Mobile Computing,2010,9(6):897-910.
[4] Hu Lingxuan,Evans D.Localization for mobile sensor networks [C] //Proceedings of Tenth Annual International Conference on Mobile Computing and Networking.Philadelphia,PA,USA,2004:45-57.
[5] Baggio A,Langendoen K.Monte-Carlo localization for mobile wireless sensor networks[C] //Proceedings of 2nd International Conference on Mobile Ad-Hoc and Sensor Networks.Hong Kong,China,2006:317-328.
[6] Stevens-Navarro E,Vivekanandan V,Wong V W S.Dual and mixture Monte Carlo localization algorithms for mobile wireless sensor networks[C] //Proceedings of IEEE Wireless Communications and Networking Conference.Hong Kong,China,2007:4027-4031.
[7] Rudafshani M,Datta S.Localization in wireless sensor networks [C] //Proceedings of the 6th International Conference on Information Processing in Sensor Networks.Cambridge,MA,USA,2007:51-60.
[8] Dil B,Dulman S,Havinga P.Range-based localization in mobile sensor networks[C] //Proceedings of 3rd European Workshop on Wireless Sensor Networks.Zurich,Switzerland,2006,3868:164-179.
[9] Benkic K,Malajner M,Planinsic P,et al.Using RSSI value for distance estimation in Wireless sensor networks based on ZigBee[C] //Proceedings of 15th International Conference on Systems,Signals and Image Processing.Bratislava,Slovakia,2008:303-306.
[10] Savvides A,Park H,Srivastava M B.The n-hop multilateration primitive for node localization problems[J].Mobile Networks and Applications,2003,8(4):443-451.
[11] Fox D.Adapting the sample size in particle filters through KLD-sampling[J].Robotics Research,2003,22(12):985-1003.
[12] Li Changgeng,Chen Juan,Li Xinbing.A new node localization algorithm of wireless sensor networks[C] //Proceedings of 2009 World Congress on Computer Science and Information Engineering. Los Angeles,CA,USA,2009:334-338.
[13] Hu Lingxuan,Evans D.Localization for mobile sensor networks [EB/OL].(2004-10-01)[2011-11-26].http://www.cs.virginia.edu/mcl.
[14] Camp T,Boleng J,Davies V.A survey of mobility models for ad hoc networks research[J].Wireless Communications and Mobile Computing,2002,2(5):483-502.
[15] Langendoen K,Reijers N.Distributed localization in wireless sensor networks:a quantitative comparison[J].Computer Networks,2003,43(4):499-518.

备注/Memo

备注/Memo:
作者简介: 李刚(1983—),男,博士生; 陈俊杰(联系人),男,博士,教授,博士生导师,inschenjj@seu.edu.cn.
基金项目: 江苏省科技支撑计划资助项目(BE2011340)、江苏省自然科学基金资助项目(BK2010196).
引文格式: 李刚,陈俊杰.一种基于测距的蒙特卡罗盒定位算法[J].东南大学学报:自然科学版,2012,42(6):1105-1110. [doi:10.3969/j.issn.1001-0505.2012.06.016]
更新日期/Last Update: 2012-11-20