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基于分层优化的多源融合定位方法

刘傲 修春娣

刘傲,修春娣. 基于分层优化的多源融合定位方法[J]. 北京航空航天大学学报,2023,49(5):1176-1183 doi: 10.13700/j.bh.1001-5965.2021.0390
引用本文: 刘傲,修春娣. 基于分层优化的多源融合定位方法[J]. 北京航空航天大学学报,2023,49(5):1176-1183 doi: 10.13700/j.bh.1001-5965.2021.0390
LIU A,XIU C D. Multi-source fusion positioning method based on hierarchical optimization[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(5):1176-1183 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0390
Citation: LIU A,XIU C D. Multi-source fusion positioning method based on hierarchical optimization[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(5):1176-1183 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0390

基于分层优化的多源融合定位方法

doi: 10.13700/j.bh.1001-5965.2021.0390
基金项目: 国家PNT体系弹性化架构设计与关键技术示范验证(2020YFB0505800)
详细信息
    通讯作者:

    E-mail:xcd@buaa.edu.cn

  • 中图分类号: TP391

Multi-source fusion positioning method based on hierarchical optimization

Funds: Elastic Architecture Design and Key Technology Verification of National PNT System (2020YFB0505800)
More Information
  • 摘要:

    为实现复杂室内环境下行人的精确连续定位,提出一种基于分层优化的多源融合定位方法。先利用Wi-Fi定位结果约束地磁匹配范围,再将粒子群优化(PSO)引入BP-AdaBoost集成学习算法,利用优化后的BP-AdaBoost-PSO算法融合Wi-Fi定位结果与约束后的地磁匹配定位结果。最后利用粒子滤波(PF)实现上述融合结果与行人航位推算(PDR)结果的融合定位。仿真结果表明:所提方法能够有效提升行人运动状态下的连续定位精度,并具有较好的鲁棒性。

     

  • 图 1  基于Wi-Fi约束的地磁定位方法

    Figure 1.  Wi-Fi constraint-based geomagetic positioning method

    图 2  BP神经网络拓扑结构图

    Figure 2.  Topological structure of BP neural network

    图 3  PDR定位原理

    Figure 3.  Positioning principle of PDR

    图 4  融合定位方法框架

    Figure 4.  Framework of fusion positioning algorithm

    图 5  定位场景1平面图

    Figure 5.  Plan of positioning site 1

    图 6  定位场景2平面图

    Figure 6.  Plan of positioning site 2

    图 7  不同地磁定位方法定位误差的CDF曲线

    Figure 7.  CDF curves of position errors with different geomagnetic positioning algorithm

    图 8  神经网络融合算法定位误差的CDF曲线

    Figure 8.  CDF curves of position errors of neural network fusion algorithms

    图 9  执行耗时示意图

    Figure 9.  Diagram of execution time

    图 10  定位轨迹对比

    Figure 10.  Comparison of positioning trajectories

    图 11  定位误差CDF曲线对比

    Figure 11.  Comparison of CDF curves of position errors

    表  1  不同地磁定位方法的平均定位误差

    Table  1.   Average position errors of different geomagnetic positioning algorithms m

    定位法平均定位误差
    定位场景1定位场景2
    k-NN算法7.417.32
    基于Wi-Fi约束的方法2.041.97
    下载: 导出CSV

    表  2  神经网络融合算法的平均定位误差

    Table  2.   Average position errors of neural network fusion algorithms m

    定位法平均定位误差
    定位场景1定位场景2
    BP1.851.83
    BP-AdaBoost1.731.68
    BP-AdaBoost-PSO1.711.66
    下载: 导出CSV

    表  3  平均执行耗时

    Table  3.   Average execution time s

    定位算法平均执行耗时
    定位场景1定位场景2
    BP3.483.46
    BP-AdaBoost6.886.89
    BP-AdaBoost-PSO4.914.90
    下载: 导出CSV

    表  4  融合定位的平均定位误差

    Table  4.   Average position errors of fusion positioning algorithms m

    定位法平均定位误差
    定位场景1定位场景2
    PDR2.562.61
    BP-AdaBoost-PSO1.711.66
    本文方法1.271.25
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-07-09
  • 录用日期:  2021-10-29
  • 网络出版日期:  2021-11-11
  • 整期出版日期:  2023-05-31

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