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一种基于非视距误差补偿的协同定位算法

张然 宋来亮 冉龙俊

张然, 宋来亮, 冉龙俊等 . 一种基于非视距误差补偿的协同定位算法[J]. 北京航空航天大学学报, 2017, 43(7): 1426-1432. doi: 10.13700/j.bh.1001-5965.2016.0557
引用本文: 张然, 宋来亮, 冉龙俊等 . 一种基于非视距误差补偿的协同定位算法[J]. 北京航空航天大学学报, 2017, 43(7): 1426-1432. doi: 10.13700/j.bh.1001-5965.2016.0557
ZHANG Ran, SONG Lailiang, RAN Longjunet al. A collaborative localization algorithm based on non-line-of-sight error compensation[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(7): 1426-1432. doi: 10.13700/j.bh.1001-5965.2016.0557(in Chinese)
Citation: ZHANG Ran, SONG Lailiang, RAN Longjunet al. A collaborative localization algorithm based on non-line-of-sight error compensation[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(7): 1426-1432. doi: 10.13700/j.bh.1001-5965.2016.0557(in Chinese)

一种基于非视距误差补偿的协同定位算法

doi: 10.13700/j.bh.1001-5965.2016.0557
详细信息
    作者简介:

    张然  女, 硕士研究生。主要研究方向:基于超宽带的室内导航定位技术

    宋来亮  男, 讲师。主要研究方向:惯性导航与组合导航

    冉龙俊  男, 博士。主要研究方向:光学捷联惯性导航系统

    通讯作者:

    宋来亮, E-mail:songlailiang@buaa.edu.cn

  • 中图分类号: TN929.5

A collaborative localization algorithm based on non-line-of-sight error compensation

More Information
  • 摘要:

    非视距(NLOS)误差对超宽带(UWB)室内定位技术的定位精度有很大影响。针对此问题,根据NLOS环境下附加时延和由信道决定的均方根时延扩展的联合统计特性,估计NLOS误差的均值和方差,对定位算法测量值和系统测量误差协方差进行修正,并采用时变权重的粒子群算法与Chan算法相结合的协同定位算法进行定位计算,具有良好的全局搜索与局部搜索最优解的能力。仿真结果表明,在NLOS环境下,相比于单一算法,协同算法定位精度提高30%左右,在一定程度上抑制了NLOS误差的影响,满足室内定位的要求。

     

  • 图 1  粒子群算法流程图

    Figure 1.  Flowchart of particle swarm algorithm

    图 2  CM1信道和CM2信道冲激响应

    Figure 2.  Impulse response of CM1 channel and CM2 channel

    图 3  CM1信道和CM2信道基于脉冲序列最大能量到达时间测距算法的能量块图

    Figure 3.  Energy block diagram of maximum energy arrival time ranging algorithm based on pulse sequence of CM1 channel and CM2 channel

    图 4  基站位置信息

    Figure 4.  Location information of base station

    图 5  Chan算法和粒子群算法在系统测量误差不同时的定位性能对比

    Figure 5.  Comparison of positioning performance between Chan algorithm and particle swarm algorithm with different system measurement errors

    图 6  Chan算法和粒子群算法在LOS环境与NLOS环境定位性能的对比

    Figure 6.  Comparison of positioning performance in LOS and NLOS environment between Chan algorithm and particle swarm algorithm

    图 7  Chan算法和粒子群算法NLOS误差补偿后和协同定位算法之间的定位性能对比

    Figure 7.  Comparison of positioning performance among Chan algorithm after NLOS error compensation, particle swarm algorithm after NLOS error compensation, and collaborative localization algorithm

    图 8  粒子群算法和协同定位算法的收敛曲线对比

    Figure 8.  Comparison of convergence curves between particle swarm algorithm and collaborative localization algorithm

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出版历程
  • 收稿日期:  2016-06-29
  • 录用日期:  2016-10-14
  • 刊出日期:  2017-07-20

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