基于RSSI辅助的精确测距混合定位算法
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摘要
针对传统的超宽带(UWB)室内定位方法中,UWB信号极易被遮挡,满足不了3个以上有效测距信息,导致无法精确定位的问题,提出了一种基于接收信号强度(RSSI)辅助的精确测距混合定位算法。该算法通过对数正态模型,将无线网络(wireless LAN, WLAN)中测量的RSSI转换为距离信息,通过构建距离差的代价函数,结合单个UWB基站的精确测距,利用搜索方法,实现了在多个RSSI测量值辅助下,一个UWB测距基站便可完成精确定位。该算法与三边测量定位中的最小二乘估计算法和最大似然估计算法对比,定位精度在任意网络环境下均优于最小二乘估计算法,且在定位精度相似的情况下,计算量远少于最大似然估计算法。
In ultra-wideband(UWB)-based indoor positioning, the number of base stations used for location calculating is always affected by non-line-of-sight(NLOS) propagation or sheltering, and thus leading to no solution of location equations. This paper proposes a precise indoor localization algorithm based on received signal strength indicator(RSSI) and UWB ranging techniques. The multiple RSSI measurements of wireless local area network(WLAN) is transformed into the corresponding distances, which can be used to improve the positioning accuracy of the UWB. Our technique can localize targets by minimizing the positioning errors with only one UWB base station. Compared with the existing least squares(LS) and maximization likelihood(ML) algorithms, our method achieves better performances in arbitrary structure of base stations with low computational complexity.
In ultra-wideband(UWB)-based indoor positioning, the number of base stations used for location calculating is always affected by non-line-of-sight(NLOS) propagation or sheltering, and thus leading to no solution of location equations. This paper proposes a precise indoor localization algorithm based on received signal strength indicator(RSSI) and UWB ranging techniques. The multiple RSSI measurements of wireless local area network(WLAN) is transformed into the corresponding distances, which can be used to improve the positioning accuracy of the UWB. Our technique can localize targets by minimizing the positioning errors with only one UWB base station. Compared with the existing least squares(LS) and maximization likelihood(ML) algorithms, our method achieves better performances in arbitrary structure of base stations with low computational complexity.
引文
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[2]LADD A M,BEKRIS K E,RUDYS A P,et al.On the feasibility of using wireless ethernet for indoor localization[J].Robotics&Automation IEEE Transactions on,2004,20(3):555-559.
[3]BAHL P,PADMANABHAN V N.Radar:an in-building RFbased user location and tracking system[C]//Proc IEEEInfocom.[S.l.]:IEEE,2000,2:775-784.
[4]KOLODZIEJ K W,HJELM J.Local positioning systems:LBS applications and services[M].[S.l.]:CRC press,2006:101-158.
[5]PRIYANTHA N,CHAKRABORTY A,BALAKRISHNANH.The Cricket location-support system[C]//International Conference on Mobile Computing and Networking.[S.l.]:ACM,2000:32-43.
[6]黄正宇,陈益强,刘军发,等.基于众包数据的室内定位方法和平台[J].地球信息科学学报,2016,18(11):1476-1484.HUANG Zheng-yu,CHEN Yi-qiang,LIU Jun-fa,et al.Indoor localization method and platform based on crowdsourcingdata[J].Journal of Geo-information Science,2016,18(11):1476-1784.
[7]黄正勇.基于众包的室内定位关键技术研究[D].上海:上海交通大学,2015.HUANG Zheng-yong.Research on key technologies of crowdsourcing-based indoor localization system[D].Shanghai:Shanghai Jiao Tong University,2015.
[8]郭伟龙.融合多源信息的室内定位算法研究[D].杭州:浙江大学,2016.GUO Wei-long.Research on indoor localization based on fusion of multi-source information[D].Hangzhou:Zhejiang University,2016.
[9]周启臻,邢建春,李决龙,等.一种多元信息融合的室内定位方法[J].微型机与应用,2016,35(22):72-76.ZHOU Qi-zhen,XING Jian-chun,LI Jue-long,et al.An indoor localization method based on multi-data fusion[J].Microcomputer&Its Applications,2016,35(22):72-76.
[10]张科.基于多源信息融合的移动定位优化技术研究[D].北京:北京邮电大学,2013.ZHANG Ke.Research on mobile positioning optimization technology based on multi-source information fusion[D].Beijing:Beijing University of Posts and Telecommunications,2013.
[11]黄承恺.多传感器信息融合算法在室内定位中的应用[D].北京:北京邮电大学,2015.HUANG Cheng-kai.The application of multi-sensor information fusion algorithm for indoor positioning system[D].Beijing:Beijing University of Posts and Telecommunications,2015.
[12]RAPPAPORT S T.Wireless communications:Principles and practice[M].[S.l.]:Prentice Hall,2001.
[13]PATWARI N,PERKINS M,CORREAL N S,et al.Relative location estimation in wireless sensor networks[J].IEEE Transactions on Signal Processing,2015,51(8):2137-2148.
[14]LUO R C,CHEN O,LIN P H.Indoor robot/human localization using dynamic triangulation and wireless Pyroelectric Infrared sensory fusion approaches[C]//IEEEInternational Conference on Robotics and Automation.[S.l.]:IEEE,2012:1359-1364.
[15]YOON C,CHA H.Experimental analysis of IEEE802.15.4a CSS ranging and its implications[J].Computer Communications,2011,34(11):1361-1374.