基于指纹算法的无线室内定位技术

doi:10.11959/j.issn.1000-0801.2016267

摘要/Abstract

摘要：

随着人们对基于位置的服务（LBS）需求日益增大，室内定位逐渐成为用户定位领域的研究热点，而指纹定位因具有定位精度高、普适性强和无需额外设备等优点而受到大多数研究者的青睐。首先详细综述了各种主流室内位置指纹定位技术的定位原理，然后归纳了现有的室内定位算法的原理及发展现状，最后搭建楼宇通道的测试场景，对各种典型室内定位算法进行测试验证和比较分析，为室内定位技术的研究与应用人员提供参考。

关键词: 室内定位, 位置指纹, 定位精度

Abstract:

As people's demand for location based service（LBS）is increasing, indoor localization has gradually becomes a hot research topic in the field of user location. And the fingerprint orientation is very much popular among most of the researchers, not only because it has high positioning accuracy and strong universality, but also because it doesn't need additional equipment. Firstly, the positioning principle of various kinds of main stream indoor location fingerprint positioning technology was summarized. Then the principle of the existing indoor localization algorithm and its developing condition at present were summarized. Finally, by setting up buildings channel test scenes, all kinds of typical indoor localization algorithm are testified, validated, compared and analyzed. The reference was provided for researchers and people who is researching on indoor location technology.

Key words: indoor localization, location fingerprint, positioning accuracy

张梦丹,卢光跃,王宏刚,刘继明. 基于指纹算法的无线室内定位技术[J]. 电信科学, 2016, 32(10): 77-86.

Mengdan ZHANG,Guangyue LU,Honggang WANG,Jiming LIU. Wireless indoor localization technology based on fingerprint algorithm[J]. Telecommunications Science, 2016, 32(10): 77-86.

图/表 14

图1

表1

图2

图3

表2

表3

图4

图5

表4

表5

图6

图7

图8

表6

参考文献 49

[1]	True Position I. True position guide to location technologies[R]. [S.l.:s.n.], 2009.
[2]	WILLIAMS D H . It's the（LBS）applications, stupid[R].[S.l.:s.n.], 2003.
[3]	刘君 . 室内定位系统研究[D]. 合肥：安徽大学， 2011:6-8. LIU J . Indoor positioning system[D]. Anhui University, 2011:6-8.
[4]	陈国平，马耀辉，张百珂 . 基于指纹技术的蓝牙室内定位系统[J]. 电子技术应用， 2013,39(3):104-107. CHEN G P , MA Y H , ZHANG B K . Bluetooth indoor positioning system based on fingerprint technology[J]. Application of Electronic Technique, 2013,39(3):104-107.
[5]	刘鹏，卢潭城，高翔 . 基于射频识别的室内定位技术综述[J]. 太赫兹科学与电子信息学报， 2014(2):195-201. LIU P , LU T C , GAO X . Summary of indoor positioning technology based on radio frequency identification[J]. Terahertz Science and Electronic Information Science News, 2014(2):195-201.
[6]	国家测绘地理信息．介绍几种室内定位技术[EB/OL]. [2016-07-10]. .国家测绘地理信息．介绍几种室内定位技术[EB/OL]. [2016-07-10]. .
[7]	YOUNG D P , KELLEP C M , BCLISS D W , et al. Ultra-wideband（UWB）transmitter location using time difference of arrival（TDOA）techniques[C]// 37th Asilomar Conference on Signals, Systems and Computers, April 3-5,2004, Pacific Grove, USA. New Jersey:IEEE Press, 2004: 1225-1229.
[8]	EVENNOU F , MARX F , NACIVET S . An experimental TDOA UWB location system for NLOS environment[C]// IEEE 62th Vehicular Technology Conference Fall, March 11-13,2005, Dallas, Texas, USA. New Jersey:IEEE Press, 2005: 420-423.
[9]	DOHERTY L . Algorithms for position and data recovery in wireless sensor networks[D]. Berkeley:UC Berkeley, 2000:1-54.
[10]	YU H G , HUANG G M , GAO J , et al. Approximate maximum likelihood algorithm for moving source localization using TDOA and FDOA measurements[J]. Chinese Journal of Aeronautic, 2012,25(4): 593-597.
[11]	叶蔚 . 室内无线定位的研究[D]. 广州：华南理工大学， 2010. YE W . Study indoor wireless location[D]. Guangzhou: South China University of Technology, 2010.
[12]	杨东勇，顾东袁，傅晓捷 . 一种基于RSSI 相似度的室内定位算法[J]. 传感技术学报， 2009,22(2):264-268. YANG D Y , GU D Y , FU X J . A kind of interior RSSI location algorithm similarity[J]. Based Sensing Technology, 2009,22(2):264-268.
[13]	华蕊，郝永平，杨芳 . 超声波定位系统的设计[J]. 国外电子测量技术， 2009(6):65-67. HUA R , HAO Y P , YANG F . Design of ultrasonic positioning system[J]. Foreign Electronic Measurement Technology, 2009(6):65-67.
[14]	BAHL P , PADMANABHAN V N . Radar: an in-building RF-based user location and tracking system[J]. Institute of Electrical ＆ Electronics Engineers Inc, 2000: 775-784.
[15]	YOUSSEF M , AGRAWALAA SHANKAR A U . WLAN location determination via clustering and probability Distributions[C]// IEEE International Conference on Pervasive Computing and Communications（PerCom）, March 11-19,2003, Fort Worth,Texas, USA. New Jersey:IEEE Press, 2003.
[16]	CASTRO P , CHIU P , KREMENE K T , et al. A probabilistic room location service for wireless networked environments[C]// Third International Conference on Ubiquitous Computing, September 30-October 2 Atlanta, Georgia, USA. New York:ACM Press, 2001: 18-34.
[17]	VICTOR L , XU K , CYNTHIA G U . A difference model for open WLAN environment based location service[J]. Submitted to IEEE Infocom, 2006.
[18]	YONG L S , YU B X , LIU M , et al. KNN-FCM hybrid algorithm for indoor location in WLAN[C]// International Conference on Power Electronics and Intelligent Transportation System, January 5-8,2010, Fort Worth,Texas,USA. New Jersey:IEEE Press, 2010.
[19]	WU D , XU Y , MA L . Research on RSS based indoor location method[C]// Pacific-Asia Conference on Knowledge Engineering and Software Engineering, June 3-5,2009, Fort Worth, Texas, USA. New Jersey:IEEE Press, 2009:205-208.
[20]	DENKOVSKID , ANGJELICINOSKIM , ATANASOVSKIV , et al. Practical assessment of RSS-based localization in indoor environments[C]// IEEE Military Communications Conference MILCOM, July 3-5,2012, Orlando, USA. New Jersey:IEEE Press, 2012.
[21]	WU C , YANG Z , LIU Y , et al. WILL: wireless indoor localization without site survey[J]. IEEE Transactions on Parallel and Distributed Systems, 2013,24(4): 839-848.
[22]	WANG H , SEN S , ELGOHARY A , et al. No need to War-drive:unsupervised indoor localization[C]// 10th International Conference on Mobile Systems, Applications, and Services, January 11-13,2012, Fort Worth, Texas, USA. New York:ACM Press, 2012: 197-210.
[23]	Sorwitor. Soiruitor sense RTLS[EB/OL]. (2011-10-30) [2016-07-10]. .
[24]	孙玉山，代天娇，赵志平 . 水下机器人航位推算导航系统及误差分析[J]. 船舶工程， 2010(5):67-72. SUN Y S , DAI T J , ZHAO Z P . Underwater robots dead reckoning navigation system and error analysis [J]. Marine Engineering, 2010(5):67-72.
[25]	WOODMAN O J . An introduction to inertial navigation:UCAMCL-TR-696[R]. Cambridge:University of Cambridge Press, 2007.
[26]	张明华 . 基于WLAN 的室内定位技术研究[D]. 上海：上海交通大学， 2009. ZHANG M H . Study WLAN based indoor positioning technology[D]. Shanghai: Shanghai Jiaotong University, 2009.
[27]	JAIN A K , FENG J , NANDAKUMAR K . Fingerprint matching[J]. Computer, 2010,43(2): 36-44.
[28]	梁莹 . NS/地磁匹配组合导航系统技术研究[D]. 哈尔滨：哈尔滨工程大学， 2010. LIANG Y . INS/geomagnetic integrated navigation system matching technology research[D]. Harbin: Harbin Engineering University, 2010.
[29]	王欣 . 基于地磁场和 RSSI 的室内定位算法设计与实现[D]. 杭州：杭州电子科技大学， 2014. WANG X . And implementation of indoor location algorithm based on the geomagnetic field and RSSI[D]. Hangzhou:Hangzhou University of Electronic Science and Technology, 2014.
[30]	MOUSTAFA Y , MOHAMED A , ASHOK A . Multivariate analysis for probabilistic WLAN location determination systems[C]// The Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, August 7-8,2005, Fort Worth,Texas,USA. New Jersey:IEEE Press, 2005: 353-362.
[31]	詹杰，刘宏立，刘述钢，等. 基于RSSI 的动态权重定位算法研究[J]. 电子学报， 2011,39(1):82-88. ZHAN J , LIU H L , LIU S G , et al. Study algorithm based RSSI's dynamic Weights location[J]. Journal of Electronics, 2011,39(1):82-88.
[32]	SECO F , JIMENEZ A , PRIETO C , et al. A survey of mathematical methods for indoor localization[J]. Intelligent Signal Processing, 2009: 9-14.
[33]	赵方，罗海勇，林权，等. 基于核函数法及马尔可夫链的节点定位算法[J]. 通信学报， 2010(11):195-204. ZHAO F , LUO H Y , LIN Q , et al. Localization algorithm based on kernel functions method and node Markov chain[J]. Journal of Communications, 2010(11):195-204.
[34]	SUYKENS J , VAN G T , DE B P , et al. Least square support vector machines[J]. Singapore: World Scientific Publishing, 2002: 71-111.
[35]	WARD A , JONES A , HOPPER A . A new location technique for the active office[J]. IEEE Personal Communications, 1997,4(5): 42-47.
[36]	王淑婷 . 基于位置指纹的Wi-Fi定位算法研究[J]. 长春：吉林大学， 2015. WANG S T . Study Wi-Fi location algorithm based on location fingerprint[J]. Changchun: Jilin University, 2015.
[37]	蔡朝晖，夏溪，胡波，等. 室内信号强度指纹定位算法改进[J]. 计算机科学， 2014,41(11):178-181. CAI C H , XIA X , HU B , et a. Indoor fingerprint localization algorithm improved based on signal strength[J]. Computer Science, 2014,41(11):178-181.
[38]	李娟娟，张金艺，张秉煌，等. 蓝牙标准规范下的模糊指纹定位算法[J]. 上海大学学报（自然科学版）， 2013,19(2):126-131. LI J J , ZHAN G J Y , ZHANG B H , et a. Fuzzy fingerprint positioned algorithm under the Bluetooth standard specification[J]. Shanghai University（Natural Science）, 2013,19(2):126-131.
[39]	刘广孚，田冀，陶凤阳，等. 基于样本内主成分分析的潜油电泵偏磨诊断[J]. 仪器仪表学报， 2012,33(7):1527-1532. LIU G F , TIAN J , TAO F Y , et a. Partial friction diagnosis of electric submersible pump based on principal component analysis in sample[J]. component analysis in sample[J]. Chinese Journal of Scientific Instrument, 2012,33(7):1527-1532.
[40]	张兴 . WLAN 室内定位信号特征提取算法研究[J]. 哈尔滨：哈尔滨工业大学， 2013. ZHANG X . Research on WLAN indoor positioning signal feature extraction algorithm[J]. Harbin: Harbin Institute of Technology, 2013.
[41]	VAPNIK V , VASHIST A . A new learning paradigm: learning using privileged information[J]. Neural Networks, 2009,22(5): 544-557.
[42]	OUYANG R W , WONG A K S , WOO K T . Indoor localization via discriminatively regularized least square classification[J]. International Journal of Wireless Information Networks, 2011,18(2): 57-72.
[43]	SENGUR A . Multiclass least-squares support vector machines for analog modulation classification[J]. Expert Systems with Applications, 2009,36(3): 6681-6685.
[44]	姚全珠，蔡捷 . 基于PSO 的LS-SVM 特征选择与参数优化算法[J]. 计算机工程与应用， 2010,46(1):134-136. YAO Q Z , CAI J . Algorithm of LS-SVM feature selection and parameters' optimization of based on PSO[J]. Computer Engineering and Applications, 2010,46(1):134-136.
[45]	黄震，罗中良，陈治明 . 基于优化最小二乘支持向量机的室内定位算法[J]. 中山大学学报（自然科学版）， 2016,55(2):48-51. HUANG Z , LUO Z L , CHEN Z M . Indoor localization algorithm based on optimizationleast squares support vector machine[J]. Journal of Sun Yat-sen（Natural Science）, 2016,55(2):48-51.
[46]	HIGHTOWER J , BORRIELLO G . Location systems for ubiquitous computing[J]. Computer, 2001,34(8): 57-66.
[47]	GUVENC I , SAHINOGLU Z . Multi-scale energy products for TOA estimation in UWB system[C]// IEEE Global Telecomm Conf,（GLOBE_COM）, January 11-17,2005, Fort Worth, Texas, USA. New Jersey:IEEE Press, 2005.
[48]	PRIYANTHA N B , CHAKRABORTY A , BALAKRISHNAN H . The cricket location-support system[C]// MOBICOM, August 2-7,2000, Boston, USA. New York:ACM Press, 2000: 32-43.
[49]	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， 2007,37(6):68-71,75. ZHANG M H , ZHANG S S , CAO J . Indoor localization based on signal strength in WLAN[J]. Computer Science, 2007,37(6):68-71,75.

算法	特点	匹配原则	位置估计	工作量	定位误差比较
NN	确定性指纹匹配算法	欧式距离最小	欧式距离最小的一个参考点的位置	大	精度低
KNN	确定性指纹匹配算法	欧式距离最小	欧式距离最小的K个参考点的位置平均值	大	精度稍高
WKNN	确定性指纹匹配算法	欧式距离最小	欧式距离最小的K个参考点的位置加权	大	精度高
SVM	机器学习方法	结构风险最小化原则	决策函数进行估计	小	精度很低
Bayes	概率型算法	贝叶斯定理	后验概率最大的指纹点	大	精度稍低

参数K的取值	定位误差/m
2	0.686 520
3	0.605 201
4	0.567 716
5	0.647 155

参数K的取值	定位误差/m
2	0.425 592
3	0.371 619
4	0.347 108
5	0.390 232

惩罚因子C	高斯核函数参数δ	定位误差/m
13.939	9.180	7.082 811
13.939	10.0	7.082 693
13.939	8.7	7.082 881
14.5	9.180	7.082 811
13.0	8.7	7.082 878

惩罚因子C	高斯核函数参数δ	维数	定位误差/m
13.939	9.180	4	7.078 543
13.939	10.0	4	7.078 190
13.939	8.7	4	7.078 776
14.5	9.180	4	7.078 537
13.0	8.7	4	7.078 786
13.939	9.180	6	7.082 811
13.939	10.0	6	7.082 694
13.939	8.7	6	7.082 881
14.5	9.180	6	7.082 812
13.0	8.7	6	7.082 878