改进的室内三维模糊位置指纹定位算法

doi:10.6040/j.issn.1672-3961.3.2014.109

山东大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (3): 22-27.doi: 10.6040/j.issn.1672-3961.3.2014.109

改进的室内三维模糊位置指纹定位算法

曾碧^1,2, 毛勤^1,2

1. 广东工业大学计算机学院, 广东广州 510000;
2. 广东省物联网与控制专用芯片及系统智能化工程技术研究中心, 广东广州 510000

收稿日期:2014-10-08 修回日期:2015-05-11 出版日期:2015-06-20 发布日期:2014-10-08
通讯作者: 毛勤(1991- ),女,湖南岳阳人,硕士研究生,主要研究方向为基于ZigBee的室内无线定位技术. E-mail:942407600@qq.com E-mail:942407600@qq.com
作者简介:曾碧(1963- ),女,广东广州人,教授,博士,硕导, 主要研究方向为嵌入式系统与智能技术,智能计算与智能机器人. E-mail:272070973@qq.com
基金资助:
国家自然科学基金资助项目(61173046);广东省自然科学基金资助项目(S2012040007326)

Improved indoor 3-D fuzzy position fingerprint localization algorithm

ZENG Bi^1,2, MAO Qin^1,2

1. College of Computer Science, Guangdong University of Technology, Guangzhou 510000, Guangdong, China;
2. Guangdong Provincial Research Center of Internet of Things, Control Special Chip and Intelligent System Engineering Technology, Guangzhou 510000, Guangdong, China

Received:2014-10-08 Revised:2015-05-11 Online:2015-06-20 Published:2014-10-08

摘要/Abstract

摘要： 提出了改进的三维空间模糊指纹定位方法(ITF)。该方法首先采用高斯模型对样本节点和未知节点的RSSI值进行过滤,建立样本点的指纹数据库,并将传统的求解高次坐标问题转换成空间隶属度的问题。利用模糊匹配算法计算未知节点与指纹库中各个已知样本点的贴近度,通过贴近度权系数定位未知节点。实验结果表明该定位方法比传统定位算法在降低误差方面具有更高的性能。

关键词: 模糊匹配, RSSI(received signal strength indicator), 位置指纹, ITF(indoor three-dimensional fuzzy), 贴进度权系数, 高斯模型, 三维空间

Abstract: An improved indoor three-dimensional fuzzy position fingerprint localization method named ITF was proposed to improve the positioning accuracy. Gaussian model was used for filtering the received signal strength of sample nodes and unknown nodes to establish the fingerprint database for sample nodes. Then the problem of solving high order coordinates was transformed into the problem of space membership degree. The fuzzy neartude weights of unknown nodes and sample nodes were calculated, which could determine the coordinates of unknown points. The experimentalresult proved that ITF had higher performance in reducing the error than other traditional algorithms.

Key words: 3-D space, ITF(indoor three-dimensional fuzzy), RSSI(received signal strength indicator), fuzzy neartude weights, fuzzy matching, position fingerprint, Gaussian model

中图分类号:

TP391

曾碧, 毛勤. 改进的室内三维模糊位置指纹定位算法[J]. 山东大学学报(工学版), 2015, 45(3): 22-27.

ZENG Bi, MAO Qin. Improved indoor 3-D fuzzy position fingerprint localization algorithm[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2015, 45(3): 22-27.

参考文献

[1] 张顺扬. ZigBee无线传感器网络研究及仿真[D]. 广州:广东工业大学, 2008. ZHANG Shunyang. Research and simulation of zigbee wireless sensor network[D].Guangzhou:Guangdong University of Technology, 2008.
[2] 孙晓玲,李伟勤. 基于RFID的二维室内定位算法的实现[J]. 现代电子技术, 2010(24):90-92. SUN Xiaoling, LI Weiqin. The implementation of 2-D indoor location algorithm based on RFID[J]. Journal of Modern Electronic Technology, 2010(24):90-92.
[3] 高睿劼,黄鲁,朱警怡. UWB室内定位系统的射频收发机设计[J]. 微型机与应用,2013(13):87-89. GAO Ruijie, HUANG Lu, ZHU Jingyi. Design of RF transceiver in UWB positioning system[J]. Micro Computer & Applications, 2013(13):87-89.
[4] Joseph Huang, David Millman, Morgan Quigley, et al. Efficient, generalized indoor WiFi graph SLAM[J]. Robotics and Automation IEEE, 2011:1038-1043.
[5] 工福豹,史龙,任丰原.无线传感器网络中的自身定位系统和算法[J]. 软件学报, 2005(5):857-868. GONG Fubao, SHI Long, REN Fengyuan. Self-localization systems and algorithms for wireless sensor networks[J]. Journal of Software, 2005(5):857-868.
[6] 刘金龙.无线传感器网络TDOA定位算法研究[D].哈尔滨:哈尔滨工业大学,2011. LIU Jinglong. Research on TDOA localization algorithm in wireless sensor network[D]. Harbin:Harbin Institute of Technology, 2011.
[7] Carl M Wong, Geoffrey G Messier, Richard Klukas. Evaluating measurement-based AOA indoor location using WLAN infrastucture[C]//20^th International Technical Meeting of the Satellite Division of the Institute of Navigation. Texas, USA:Fort Worth Convention Center, 2007, 4:1139-1145.
[8] 叶蔚.室内无线定位的研究[D].广州:华南理工大学, 2010. YE Wei. Research on indoor wireless positioning[D]. Guangzhou: South China University of Technology, 2010.
[9] 陈丽, 王学东, 孙晶晶, 等.基于改进高斯滤波的室内无线定位算法[J].电气自动化, 2014(3):31-33+53. CHEN Li, WANG Xuedong, SUN Jingjing, et al. Indoor wireless locatlization algorithm based on improved Gaussian Filter[J]. Electrical Automation, 2014(3):31-33+53.
[10] 朱剑, 赵海, 林凯, 等. 基于WSNs的模糊三角形定位模型研究[J].东北大学学报, 2010, 31(1):35-38. ZHU Jian, ZHAO Hai, LIN Kai, et al. Research on the fuzzy triangular localization model in WSNs[J]. Journal of Northeastern University, 2010, 31 (1):35-38.
[11] 熊志广,石为人,许磊, 等.基于加权处理的三边测量定位算法[J]. 计算机工程与应用, 2010, 46(22):99-102. XIONG Zhiguang, SHI Weiren, XU Lei, et al. Trilateration localization algorithm based on weighted disposal[J].Computer Engineering & Applications, 2010, 46(22):99-102.
[12] BULUSU N, HEIDEMANN J, ESTRIN D. GPS less low cost outdoor localization for very small devices[J]. IEEE Personal Communieations, 2000, 7(5):28-34.
[13] SU J. Location technology research under the environment of WLAN[J].WSEAS Transactions on Computers, 2007, 6(8):1050-1055.
[14] EDWARDS W K.Discovery systems in ubiquitous computing[J]. IEEE Pervasive Computing, 2006, 5(2):70-77.
[15] 周艳,李海成.基于RSSI无线空间网络定位算法[J].通信学报, 2009, 30(6):75-79. ZHOU Yan, LI Haicheng. Space localization algorithm based RSSI in wirless sensor networks[J]. Journal of Communication, 2009, 30(6):75-79.
[16] 董梅,杨曾,张健, 等.基于信号强度的无线局域网定位术[J].计算机应用, 2004, 24(12):49-52. DONG Mei, YANG Zeng, ZHANG Jian, et al. Signal strength based WLAN location determination technology[J]. Computer Applications, 2004, 24(12):49-52.
[17] 朱山,刘文予.室内无线传播及覆盖性能研究[D]. 武汉:华中科技大学, 2012. ZHU Shan, LIU Wenyu. Research on indoor wireless propagation and coverage performance[D]. Wuhan: Huazhong University of Science and Technology, 2012.
[18] 陈振华,余永权,张瑞.模糊模式识别的几种模型研究[J].计算机技术与发展,2010(9):32-35. CHEN Zhenhua, YU Yongquan, ZHANG Rui. Research on several models of fuzzy pattern recognition[J]. Computer Technology & Development, 2010(9):32-35.
[19] SEOK H S, HWANG K B, ZHANG B T. Feature relevance network-based transfer learning for indoor location estimation[J]. IEEE Trans Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2011, 41(5):711-719.
[20] 邹杰,李珊君,陈晓明.一种改进的室内无线定位算法[J].计算机工程, 2011, 37(14):76-78. ZHOU Jie, LI Shanjun, CHEN Xiaoming. Improved indoor wireless localization algorithm[J]. Computer Engineering, 2011, 37(14):76-78.
[21] 杨博雄,倪玉华,刘琨, 等.基于加权三角质心RSSI算法的ZigBee室内无线定位技术研究[J].信号与系统,2012(11):31-35. YANG Boxiong, NI Yuhua, LIU Kun, et al. Study on ZigBee wireless location technology based on weighting triple centroid RSSI algorithm[J]. Signal Process & System, 2012(11):31-35.
[22] KUSHKI A, PLATANIOTIS K, VENETSANOPOULOS A. Kernel-based positioning in wireless local area networks[J]. IEEE Transactions on Mobile Computing, 2007, 6(6):689-705.
[23] ALIMORADI A, PEZESHK S, NAEIM F.Fuzzy pattern classification of strong ground motion records[J]. Journal of Earthquake Engineering, 2005, 9(3):307-332.
[24] HUANG C T, WU C H, LEE Y N, et al. A novel indoor RSS-based position location algorithm using factor graphs[J]. IEEE Transactions on Wireless Communications, 2009, 8(6):3050-3058.
[25] ZARUBA G V. Indoor location tracking using RSSI readings from a single Wi-Fi access point[J].Wireless Networks, 2007, 13(2):221-235.

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改进的室内三维模糊位置指纹定位算法

Improved indoor 3-D fuzzy position fingerprint localization algorithm

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