基于非线性RTS平滑器的RFID室内定位跟踪
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摘要
针对基于接收信号强度的传统射频识别室内定位方法目标追踪精度低,且易受多径效应和环境噪声影响的问题。结合无源超高频RFID系统链路预算模型,提出基于RTS平滑算法的RFID室内定位跟踪方法。在RTS平滑算法的前向滤波阶段采用无迹卡尔曼滤波的方法,可以解决室内定位跟踪存在的非线性问题;对目标的运动过程采取分段平滑处理,可以同时满足定位精度与实时性的需求。仿真结果表明,改进算法可以在获得较高定位精度的同时保证定位的实时性,且在复杂的环境中表现出比传统定位方法更强的抗干扰能力。
Due to traditional radio frequency identification indoor positioning methods based on received signal strength can not track the target accurately,and is easy to be affected by environmental noise and multipath effect.This paper integrated link budget model of passive ultra high frequency RFID system and proposed an RFID indoor positioning and tracking method based on RTS smoothing algorithm.In order to solve the nonlinear problem of positioning and tracking process,unscented Kalman filter was used in forward filtering stage of RTS smoothing algorithm.Meanwhile,the moving process of the target was segmented to meet the requirement of accuracy and real-time.The simulation results prove that,the revised algorithm can get better localization accuracy as well as effectiveness.Compared with the traditional one,especially in a more complex environment,the revised algorithm shows a stronger anti-jamming capability.
Due to traditional radio frequency identification indoor positioning methods based on received signal strength can not track the target accurately,and is easy to be affected by environmental noise and multipath effect.This paper integrated link budget model of passive ultra high frequency RFID system and proposed an RFID indoor positioning and tracking method based on RTS smoothing algorithm.In order to solve the nonlinear problem of positioning and tracking process,unscented Kalman filter was used in forward filtering stage of RTS smoothing algorithm.Meanwhile,the moving process of the target was segmented to meet the requirement of accuracy and real-time.The simulation results prove that,the revised algorithm can get better localization accuracy as well as effectiveness.Compared with the traditional one,especially in a more complex environment,the revised algorithm shows a stronger anti-jamming capability.
引文
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[2]冯冬青,蒋肖肖.基于GPSO的WSN节点定位技术研究[J].计算机仿真,2014,31(2):370-373.
[3]程钢,郑富娥.传感器数据定位测距算法优化仿真与研究[J].计算机仿真,2015,32(12):284-286.
[4]肖竹,谭光华,李仁发.无线传感器网络中基于超宽带的TOA/AOA联合定位研究[J].计算机研究与发展,2013,50(3):453-460.
[5]刘福铭.RFID与无线传感器网络集成技术研究与开发[D].上海交通大学,2007.
[6]邹学玉,韩付伟.基于LANDMARC的最近邻居改进算法[J].武汉大学学报,2013,59(3):255-259.
[7]葛文涛,陈俊杰.基于三边定位的WSN锚节点加权补偿算法[J].测控技术,2010,29(9):92-95.
[8]樊荣.RFID阅读器定位算法研究[D].电子科技大学,2010.
[9]V Savic,A Athalye,M Bolic.Particle filtering for indoor RFID tag tracking[J].IEEE SSP,2011-12:193-196.
[10]S Sarkkar,V V Viikari,M Huusko.Phase-Based UHF RFIDTracking With Nonlinear Kalman Filtering and Smoothing[J].IEEE Sensors Journal,2012,12(5):904-910.
[11]杨峻巍.容积Rauch-Tung-Striebel平滑器[J].电讯技术,2014,54(11)1468-1474.
[12]佘开,何怡刚,李兵.无源超高频RFID系统链路预算分析[J].仪器仪表学报,2010,31(5):974-979.
[13]佐磊,何怡刚,李兵.无源超高频射频识别系统路径损耗研究[J].物理学报,2013,62(14):150-157.
[14]佐磊,何怡刚,李兵.标签密集环境下天线互偶效应研究[J].物理学报,2013,62(4):123-131.
[15]张勇刚,黄玉龙,武哲民.一种高阶无迹卡尔曼滤波方法[J].自动化学报,2014,40(5):838-848.