留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

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
  • [1] ZEKAVAT S, BUEHRER R M, DURGIN G D, et al. An overview on position location: Past, present, future[J]. International Journal of Wireless Information Networks, 2021, 28(1): 45-76. doi: 10.1007/s10776-021-00504-z
    [2] 陈锐志, 陈亮. 基于智能手机的室内定位技术的发展现状和挑战[J]. 测绘学报, 2017, 46(10): 1316-1326. doi: 10.11947/j.AGCS.2017.20170383

    CHEN R Z, CHEN L. Indoor positioning with smartphones: The state-of-the-art and the challenges[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10): 1316-1326(in Chinese). doi: 10.11947/j.AGCS.2017.20170383
    [3] ASHRAF I, HUR S, PARK Y. Smartphone sensor based indoor positioning: Current status, opportunities, and future challenges[J]. Electronics, 2020, 9(6): 891-919. doi: 10.3390/electronics9060891
    [4] ROY P, CHOWDHURY C. A survey of machine learning techniques for indoor localization and navigation systems[J]. Journal of Intelligent & Robotic Systems, 2021, 101(3): 1-34.
    [5] MAGRIN C E, BRITO R C, TODT E. A systematic mapping study on multi-sensor fusion in wheeled mobile robot self-localization[C]//2019 Latin American Robotics Symposium, 2019 Brazilian Symposium on Robotics and 2019 Workshop on Robotics in Education. Piscataway: IEEE Press, 2020: 132-137.
    [6] YU C Y, EL-SHEIMY N, LAN H Y, et al. Map-based indoor pedestrian navigation using an auxiliary particle filter[J]. Micromachines, 2017, 8(7): 225-240. doi: 10.3390/mi8070225
    [7] LI Y, ZHUANG Y, ZHANG P, et al. An improved inertial/WiFi/magnetic fusion structure for indoor navigation[J]. Information Fusion, 2017, 34: 101-119. doi: 10.1016/j.inffus.2016.06.004
    [8] 张立志, 陈殿生, 刘维惠. 基于混合地图的护理机器人室内导航方法[J]. 北京航空航天大学学报, 2018, 44(5): 991-1000. doi: 10.13700/j.bh.1001-5965.2017.0325

    ZHANG L Z, CHEN D S, LIU W H. Care robot indoor navigation method based on hybrid map[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(5): 991-1000(in Chinese). doi: 10.13700/j.bh.1001-5965.2017.0325
    [9] ASHRAF I, HUR S, PARK Y. Application of deep convolutional neural networks and smartphone sensors for indoor localization[J]. Applied Sciences, 2019, 9(11): 2337. doi: 10.3390/app9112337
    [10] 徐岩, 李宁宁. 双层PSO-ELM融合室内定位算法[J]. 天津大学学报(自然科学与工程技术版), 2021, 54(1): 61-68.

    XU Y, LI N N. Double-layer PSO-ELM fusion indoor positioning algorithm[J]. Journal of Tianjin University (Science and Technology), 2021, 54(1): 61-68(in Chinese).
    [11] LIU S. Multi-sensor data fusion algorithm based on BP neural network[J]. Journal of Physics:Conference Series, 2020, 1584(1): 012025. doi: 10.1088/1742-6596/1584/1/012025
    [12] FREUND Y. Experiment with a new boosting algorithm[C]//Machine Learning: Proceedings of the Thirteenth International Conference. San Francisco: Margan Kaufmann, 1996: 148-156.
    [13] FRESNO J M, ROBLES G, MARTÍNEZ-TARIFA J M, et al. Survey on the performance of source localization algorithms[J]. Sensors, 2017, 17(11): 2666-2690. doi: 10.3390/s17112666
    [14] WANG Y, ZHAO H D. Improved smartphone-based indoor pedestrian dead reckoning assisted by visible light positioning[J]. IEEE Sensors Journal, 2019, 19(8): 2902-2908. doi: 10.1109/JSEN.2018.2888493
    [15] 刘飞. 多传感器融合的高精度无缝定位模型与方法研究[D]. 徐州: 中国矿业大学, 2020: 74-76.

    LIU F. Research on high precision seamless positioning model and method based on multi-sensor fusion[D]. Xuzhou: China University of Mining and Technology, 2020: 74-76(in Chinese).
    [16] WEINBERG H. Using the ADXL202 in pedometer and personal navigation applications[EB/OL]. (2021-05-30) [2021-07-01]. https://www.analog.com/media/en/technical-documentation/application-notes/513772624AN602.pdf.
    [17] 侯秀丽, 徐宝国, 周颖, 等. 基于GPS/INS/磁力计多传感器融合的连续定位[J]. 传感技术学报, 2020, 33(9): 1320-1326. doi: 10.3969/j.issn.1004-1699.2020.09.015

    HOU X L, XU B G, ZHOU Y, et al. Continuous positioning based on multi-sensor fusion of GPS/INS/magnetometer[J]. Chinese Journal of Sensors and Actuators, 2020, 33(9): 1320-1326(in Chinese). doi: 10.3969/j.issn.1004-1699.2020.09.015
  • 加载中
图(11) / 表(4)
计量
  • 文章访问数:  354
  • HTML全文浏览量:  66
  • PDF下载量:  32
  • 被引次数: 0
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

目录

    /

    返回文章
    返回
    常见问答