全向指纹和Wi-Fi感知概率的WKNN定位方法
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摘要
针对室内环境下Wi-Fi信号强度衰减受人体影响较大且存在信号缺失现象的现状,该文提出一种基于全向指纹和Wi-Fi感知概率的加权K近邻定位方法,离线阶段构建顾及用户朝向和Wi-Fi感知概率的全向指纹库,在线阶段将全向指纹库中的感知概率用于定位过程。分别开展了基于方向识别、全向指纹和该文所提定位方法的实验,该文所提的方法在K为2时定位精度最高,平均定位误差为1.42m,标准差为1.04m,45%定位结果的精度优于1m,80%定位结果的精度优于2m。实验结果表明,该方法在定位精度和稳定性方面优于基于方向识别定位方法和基于全向指纹的定位方法。基于全向指纹和Wi-Fi感知概率的WKNN定位方法能够减少用户身体遮挡和信号缺失对定位的影响,可提高Wi-Fi指纹定位的精度。
In view of the attenuation of wireless fidelity(Wi-Fi)signal intensity is greatly influenced by human body and there is signal loss phenomenon in indoor environment.A method of weighted K nearest neighbors(WKNN)positioning based on omnidirectional fingerprint and Wi-Fi sensing probability was proposed in this paper.It constructed omnidirectional fingerprint database considering user's orientation and Wi-Fi sensing probability in offline phase.Sensing probability stored in the omnidirectional fingerprint database was used for calculating coordinates in online phase.Methods based on direction recognition,omnidirectional fingerprint and the proposed method were conducted respectively.When K equals to 2,the proposed method achieved the best positioning performance with mean error of 1.42 meters and standard deviation of 1.04 meters;moreover,the accuracy of 45 percent of positioning results was better than 1 meter,the accuracy of 80 percent of positioning results was better than 2 meters.The experimental results showed that the proposed method greatly improved positioning accuracy and stability comparing to the method based on direction recognition and the method based on omnidirectional fingerprint.The WKNN positioning method based on omnidirectional fingerprint and Wi-Fi sensing probability could reduce impacts of user's body sheltering and signal loss on positioning error and improve the accuracy of Wi-Fi fingerprint-based positioning.
In view of the attenuation of wireless fidelity(Wi-Fi)signal intensity is greatly influenced by human body and there is signal loss phenomenon in indoor environment.A method of weighted K nearest neighbors(WKNN)positioning based on omnidirectional fingerprint and Wi-Fi sensing probability was proposed in this paper.It constructed omnidirectional fingerprint database considering user's orientation and Wi-Fi sensing probability in offline phase.Sensing probability stored in the omnidirectional fingerprint database was used for calculating coordinates in online phase.Methods based on direction recognition,omnidirectional fingerprint and the proposed method were conducted respectively.When K equals to 2,the proposed method achieved the best positioning performance with mean error of 1.42 meters and standard deviation of 1.04 meters;moreover,the accuracy of 45 percent of positioning results was better than 1 meter,the accuracy of 80 percent of positioning results was better than 2 meters.The experimental results showed that the proposed method greatly improved positioning accuracy and stability comparing to the method based on direction recognition and the method based on omnidirectional fingerprint.The WKNN positioning method based on omnidirectional fingerprint and Wi-Fi sensing probability could reduce impacts of user's body sheltering and signal loss on positioning error and improve the accuracy of Wi-Fi fingerprint-based positioning.
引文
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[13]曹鸿基,汪云甲,毕京学,等.一种顾及用户朝向的加权贝叶斯指纹定位方法[J/OL].[2018-04-19].http:∥kns.cnki.net/KCMS/detail/11.4415.P.20180418.1643.083.html.(CAO Hongji,WANG Yunjia,BI Jingxue,et al.A weighted Bayesian fingerprint localization method considering user’s orientation[J/OL].[2018-04-19].http:∥kns.cnki.net/KCMS/detail/11.4415.P.20180418.1643.083.html.)
[14]毕京学,汪云甲,曹鸿基,等.一种基于全向指纹库的WiFi室内定位方法[J].测绘通报,2018(2):25-29.(BI Jingxue,WANG Yunjia,CAO Hongji,et al.A method of WiFi indoor positioning based on omnidirectional fingerprint database[J].Bulletin of Surveying and Mapping,2018(2):25-29.)
[15]YOUSSEF M A,AGRAWALA A,SHANKAR A U.WLAN location determination via clustering and probability distributions[C]∥Proceedings of the First IEEE International Conference on Pervasive Computing and Communications.[S.l.]:IEEE,2003:143-150.
[2]刘克强.基于室内位置与多维情境的人类活动识别方法研究[D].徐州:中国矿业大学,2017.(LIU Keqiang.Study on human activity recognition method based on indoor location and multiple contexts[D].Xuzhou:China University of Mining and Technology,2017.)
[3] BAHL P,PADMANABHAN V N.RADAR:an inbuilding RF-based user location and tracking system[C]∥Proceedings of INFOCOM 2000.[S.l.]:IEEE,2000:775-784.
[4] YOUSSEF M,AGRAWALA A.The Horus WLAN location determination system[C]∥Proceedings of the3rd international conference on mobile systems,applications,and services.[S.l.]:ACM,2005:205-218.
[5] NI L M,LIU Y,LAU Y C,et al.LANDMARC:indoor location sensing using active RFID[J].Wireless Networks,2004,10(6):701-710.
[6] SHIN B,LEE J H,LEE T,et al.Enhanced weighted K-nearest neighbor algorithm for indoor Wi-Fi positioning systems[C]∥The 8th International Conference on Computing Technology and Information Management(ICCM),2012.[S.l.]:IEEE,2012:574-577.
[7] KAEMARUNGSI K.Distribution of WLAN received signal strength indication for indoor location determination[C]∥The 1st International Symposium on Wireless Pervasive Computing,2006.[S.l.]:IEEE,2006:6.
[8] LADD A M,BEKRIS K E,RUDYS A,et al.Roboticsbased location sensing using wireless ethernet[J].Wireless Networks,2005,11(1/2):189-204.
[9]李华亮,钱志鸿,田洪亮.基于核函数特征提取的室内定位算法研究[J].通信学报,2017,38(1):158-167.(LI Hualiang,QIAN Zhihong,TIAN Hongliang.Research on indoor localization algorithm based on kernel principal component analysis[J].Journal on Communications,2017,38(1):158-167.)
[10]CINEFRA N.An adaptive indoor positioning system based on bluetooth low energy RSSI[J/OL].[2018-04-19].http:∥xueshu.baidu.com/s?wd=An+adaptive+indoor+positioning+system+based+on+bluetooth+low+energy+RSSI&rsv_bp=0&tn=SE_baiduxueshu_c1gjeupa&rsv_spt=3&ie=utf-8&f=8&rsv_sug2=1&sc_f_para=sc_tasktype%3D%7BfirstSimpleSearch%7D.
[11]陈斌涛,刘任任,陈益强,等.动态环境中的WiFi指纹自适应室内定位方法[J].传感技术学报,2015,28(5):729-738.(CHEN Bintao,LIU Renren,CHEN Yiqiang,et al.WiFi fingerprint based self-adaptive indoor localization in the dynamic environment[[J].Chinese Journal of Sensors and Actuators,2015,28(5):729-738.)
[12]刘春燕,王坚.基于几何聚类指纹库的约束KNN室内定位模型[J].武汉大学学报(信息科学版),2014,39(11):1287-1292.(LIU Chunyan,WANG Jian.A constrained KNN Indoor positioning model based on a geometric clustering fingerprinting technique[J].Geomatics and Information Science of Wuhan University,2014,39(11):1287-1292.)
[13]曹鸿基,汪云甲,毕京学,等.一种顾及用户朝向的加权贝叶斯指纹定位方法[J/OL].[2018-04-19].http:∥kns.cnki.net/KCMS/detail/11.4415.P.20180418.1643.083.html.(CAO Hongji,WANG Yunjia,BI Jingxue,et al.A weighted Bayesian fingerprint localization method considering user’s orientation[J/OL].[2018-04-19].http:∥kns.cnki.net/KCMS/detail/11.4415.P.20180418.1643.083.html.)
[14]毕京学,汪云甲,曹鸿基,等.一种基于全向指纹库的WiFi室内定位方法[J].测绘通报,2018(2):25-29.(BI Jingxue,WANG Yunjia,CAO Hongji,et al.A method of WiFi indoor positioning based on omnidirectional fingerprint database[J].Bulletin of Surveying and Mapping,2018(2):25-29.)
[15]YOUSSEF M A,AGRAWALA A,SHANKAR A U.WLAN location determination via clustering and probability distributions[C]∥Proceedings of the First IEEE International Conference on Pervasive Computing and Communications.[S.l.]:IEEE,2003:143-150.