基于物理邻近点辅助的无线局域网指纹定位方法

doi:10.11772/j.issn.1001-9081.2014.06.1563

计算机应用 ›› 2014, Vol. 34 ›› Issue (6): 1563-1566.DOI: 10.11772/j.issn.1001-9081.2014.06.1563

基于物理邻近点辅助的无线局域网指纹定位方法

周牧,张巧,邱枫

移动通信技术重庆市重点实验室(重庆邮电大学),重庆 400065

收稿日期:2013-11-28 修回日期:2014-01-13 出版日期:2014-06-01 发布日期:2014-07-02
通讯作者: 张巧
作者简介:周牧(1984-),男,重庆人,副教授,博士,主要研究方向:视觉与无线定位、凸优化理论;张巧(1992-),女,重庆人,硕士研究生,主要研究方向:WLAN定位;邱枫(1991-),女,四川达州人,硕士研究生,主要研究方向:无缝定位。
基金资助:
重庆市科委基础与前沿研究项目;国家科技重大专项;重庆市基础与前沿研究计划项目;重庆市教委科学技术研究项目;长江学者和创新团队发展计划项目;重庆邮电大学博士启动基金;重庆邮电大学青年科学研究项目

Fingerprinting location method for WLAN using physical neighbor points information

ZHOU Mu,ZHANG Qiao,QIU Feng

Chongqing Key Laboratory of Mobile Communications Technology (Chongqing University of Posts and Telecommunications), Chongqing 400065, China

Received:2013-11-28 Revised:2014-01-13 Online:2014-06-01 Published:2014-07-02
Contact: ZHANG Qiao
Supported by:
National Natural Science Foundation;the Science and Technology Research Project of Chongqing Education Commission

摘要/Abstract

摘要：

针对传统位置指纹图中的邻近参考点（ARP）信息未能得到较好利用的问题,提出一种在离线训练阶段建立基于接收信号强度（RSS）的位置指纹库和参考点(RP)物理邻近信息库的方法。通过利用待定位点与其所对应的最近邻参考点及参考点之间的物理邻近关系,来提高指纹概率定位方法的定位精度，即：在在线定位阶段,首先根据基于信号强度概率分布的贝叶斯算法计算得到待定位点的最近邻点;然后在物理邻近信息库中搜索最近邻点的物理邻近点,并在该最近邻和物理邻近点集合中,选取特征点集合用于贝叶斯二次估计;最后将具有最大后验（MAP）概率的特征点组的均值位置作为待定位点的估计位置。实验结果表明,与传统的无物理邻近数据库的指纹概率定位方法相比，在3m内的定位精度提高了约10%,有效提高了定位的可靠性。

Abstract:

In order to make full use of Adjacent Reference Point (ARP) information in radio-map, a new method of establishing both location fingerprint database based on Received Signal Strength (RSS) and physical neighbor information database for each Reference Point (RP) in the off-line phase was proposed to improve the accuracy of fingerprinting-based probabilistic localization. In the on-line phase, based on the probability distribution of RSS, the system first used Bayesian inference to calculate the most adjacent points for each test point. Then, by using physical neighbor information database, the system found the physical adjacent points with respect to the most adjacent points. In the set of most adjacent and physical adjacent points, the system selected feature points for second Bayesian inference. Finally, the system estimated the position of each test point at the center of the group of feature points which had the Maximum A Posterior (MAP) probability. The simulation results show that, compared with the traditional method without physical neighbor information database, the proposed method can improve the localization accuracy by nearly 10%, which enhances the reliability of location determination.

中图分类号:

TP393.02

周牧张巧邱枫. 基于物理邻近点辅助的无线局域网指纹定位方法[J]. 计算机应用, 2014, 34(6): 1563-1566.

ZHOU Mu ZHANG Qiao QIU Feng. Fingerprinting location method for WLAN using physical neighbor points information[J]. Journal of Computer Applications, 2014, 34(6): 1563-1566.

参考文献

[1]YANG X, YE A, LING Y. Indoor localization algorithm based on threshold classification and signal strength weighting[J]. , 2013, 33(10): 2711-2714. (杨小亮, 叶阿勇, 凌远景. 基于阈值分类及信号强度加权的室内定位算法[J]. 计算机应用, 2013, 33(10): 2711-2714.)
[2]ZHANG M, ZHANG S, CAO J. Received-signal-strength-based indoor location in wireless LANs[J]. Computer Science, 2007, 34(6): 68-75. (张明华, 张申生, 曹健. 无线局域网中基于信号强度的室内定位[J]. 计算机科学, 2007, 34(6): 68-75.)
[3]LANG X, XU K, ZHAO M. Research & development of WLAN-based location techniques[J]. Computer Science, 2006, 33(6): 21-24. (郎昕培, 许可, 赵明. 基于无线局域网的位置定位技术研究和发展[J]. 计算机科学, 2006, 33(6): 21-24.)
[4]CHEN Y, LI X. Signal strength based indoor geolocation[J]. Acta Electronica Sinica, 2004, 32(9): 1456-1458. (陈永光, 李修和. 基于信号强度的室内定位技术[J]. 电子学报, 2004, 32(9): 1456-1458.)
[5]TANG L, XU Y, ZHOU M, et al. Research on K nearest neighbors algorithm under the indoor WLAN[J]. Computer Science, 2009, 36(4B): 54-55.(汤丽,徐玉滨,周牧,等. 基于K近邻算法的WLAN室内定位技术研究[J]. 计算机科学, 2009, 36(4B):54-55.)
[6]BAHL P, PADMANABHAN V N. RADAR: an in-building RF-based user location and tracking system[C]// INFOCOM 2000: Proceedings of the 19th Annual Joint Conference of the IEEE Computer and Communications Societies. Piscataway: IEEE Press, 2000, 2: 775-784.
[7]YOUSSEF M, AGRAWALA A. The Horus WLAN location determination system[C]// Proceedings of the 3rd International Conference on Mobile Systems, Applications, and Services. New York: ACM Press, 2005: 205-218.
[8]ZHOU R. Improve the accuracy and stability of Wi-Fi fingerprinting by applying the interior structure of buildings[J]. Journal of University of Electronic Science and Technology of China, 2013, 42(2): 295-299.(周瑞. 应用室内结构布局提高Wi-Fi定位精度和稳定性[J]. 电子科技大学学报, 2013, 42(2): 295-299.)
[9]TANG D, LIU D, XU Z, et al. Research on indoor localization technology based on nearest neighbor points database [J]. Transducer and Microsystem Technologies, 2013, 32(9): 69-71. (唐道平, 刘丹, 徐展,等. 基于近邻点数据库的室内定位技术研究[J]. 传感器与微系统, 2013, 32(9): 69-71.)
[10]TIAN L, LIU Z. Least-squares method piecewise linear fitting[J]. Computer Science, 2012, 39(6A): 482-484. (田垅, 刘宗田. 最小二乘法分段直线拟合[J]. 计算机科学, 2012, 39(6A): 482-484.)
[11]CHEN L, ZHENG Y. Study on curve fitting based on least square method[J]. Journal of Wuxi Institute of Technology, 2012, 11(5): 52-55.(陈良波,郑亚青. 基于最小二乘法的曲线拟合研究[J]. 无锡职业技术学院学报, 2012, 11(5): 52-55.)
[12]LONG H, XI S, HOU X. The algorithm and analysis from experiment data by means of minimum double multiplication[J]. Computing Technology and Automation, 2008, 27(3): 20-23.(龙辉平,习胜丰,侯新华. 实验数据的最小二乘拟合算法与分析[J]. 计算技术与自动化, 2008, 27(3): 20-23.)
[13]MA L, XU Y, WU D. A novel two-step WLAN indoor positioning method[J]. Journal of Computational Information System, 2010, 6(14): 4627-4636.

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基于物理邻近点辅助的无线局域网指纹定位方法

Fingerprinting location method for WLAN using physical neighbor points information

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