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

基于伪距残差和新息的GNSS/IMU抗差自适应定位算法

刘正午 孙蕊 蒋磊

刘正午,孙蕊,蒋磊. 基于伪距残差和新息的GNSS/IMU抗差自适应定位算法[J]. 北京航空航天大学学报,2024,50(4):1316-1324 doi: 10.13700/j.bh.1001-5965.2022.0389
引用本文: 刘正午,孙蕊,蒋磊. 基于伪距残差和新息的GNSS/IMU抗差自适应定位算法[J]. 北京航空航天大学学报,2024,50(4):1316-1324 doi: 10.13700/j.bh.1001-5965.2022.0389
LIU Z W,SUN R,JIANG L. Robust adaptive position algorithm for GNSS/IMU based on pseudorange residual and innovation[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(4):1316-1324 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0389
Citation: LIU Z W,SUN R,JIANG L. Robust adaptive position algorithm for GNSS/IMU based on pseudorange residual and innovation[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(4):1316-1324 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0389

基于伪距残差和新息的GNSS/IMU抗差自适应定位算法

doi: 10.13700/j.bh.1001-5965.2022.0389
基金项目: 国家自然科学基金(42174025,41974033);工信部民用飞机专项科研项目(MJ-2020-S-03);江苏省自然科学基金(BK20211569);江苏省“六大人才高峰”项目(KTHY-014)
详细信息
    通讯作者:

    E-mail:rui.sun@nuaa.edu.cn

  • 中图分类号: V324;P228

Robust adaptive position algorithm for GNSS/IMU based on pseudorange residual and innovation

Funds: National Natural Science Foundation of China (42174025,41974033); Horizon 2020 EU-China aviation technology cooperation project, Greener Air Traffic Operations (MJ-2020-S-03); Natural Science Foundation of Jiangsu Province (BK20211569); Jiangsu provincial Six Talent Peaks Project (KTHY-014)
More Information
  • 摘要:

    在全球导航卫星系统(GNSS)和惯性测量元件(IMU)组合导航系统中,抗差滤波和自适应滤波常被用于提高组合导航的定位精度。但是抗差滤波和自适应滤波所适用的条件不同,使用不当反而可能会降低组合导航的定位精度,针对此问题,提出基于伪距残差和新息的GNSS/IMU抗差自适应定位算法。所提算法基于伪距残差评估GNSS的定位质量,选择合适的滤波算法进行GNSS/IMU组合导航解算。在长时间GNSS定位质量较差时,基于新息和伪距残差判断是否IMU运动学推算误差大于GNSS观测值误差,从而根据判断的结果选择是否采用抗差因子。结果表明:所提算法相对于扩展卡尔曼滤波算法在东、北和天方向上分别提高36.05%、22.71%和56.22%的定位精度。

     

  • 图 1  本文算法框架

    Figure 1.  The proposed algorithm framework

    图 2  实验1中GPS/BDS三维定位误差与PDOP和平均绝对伪距残差的关系

    Figure 2.  Relationship between GPS/BDS 3D positioning error and PDOP as well as average absolute pseudorange residual in experiment 1

    图 3  实验2中GPS/BDS三维定位误差与PDOP和平均绝对伪距残差的关系

    Figure 3.  Relationship between GPS/BDS 3D positioning error and PDOP as well as average absolute pseudorange residual in experiment 2

    图 4  基于新息和伪距残差的抗差因子使用策略

    Figure 4.  Application strategy of robust factor based on innovation and pseudorange residual

    图 5  实验1中参考行驶轨迹

    Figure 5.  Reference driving track in experiment 1

    图 6  行驶轨迹部分场景

    Figure 6.  Partial scene of driving track

    图 7  实验1中卫星数目

    Figure 7.  Number of satellites in experiment 1

    图 8  实验1中4种算法的定位误差

    Figure 8.  Position errors of four algorithms in experiment 1

    图 9  实验1中本文算法滤波类型选择结果与GPS/BDS三维定位误差

    Figure 9.  The proposed algorithm filter type selection results and GPS/BDS 3D positioning errors in experiment 1

    图 10  实验2中参考行驶轨迹

    Figure 10.  Reference driving track in experiment 2

    图 11  实验2中卫星数目

    Figure 11.  Number of satellites in experiment 2

    图 12  实验2中4种算法的定位误差

    Figure 12.  Position errors of four algorithms in experiment 2

    图 13  实验2中本文算法滤波类型选择结果与GPS/BDS三维定位误差

    Figure 13.  Selection results of the proposed algorithm filter type and GPS/BDS 3D positioning errors in experiment 2

    表  1  实验1中斯皮尔曼等级相关系数

    Table  1.   Spearman rank correlation coefficients in experiment 1

    PDOP与GPS/BDS
    三维定位误差
    平均绝对伪距残差与
    GPS/BDS三维定位误差
    −0.03 0.58
    下载: 导出CSV

    表  2  实验2中斯皮尔曼等级相关系数

    Table  2.   Spearman rank correlation coefficients in experiment 2

    PDOP与GPS/BDS
    三维定位误差
    平均绝对伪距残差与GPS/BDS
    三维定位误差
    −0.24 0.91
    下载: 导出CSV

    表  3  斯皮尔曼等级相关系数相关程度[25]

    Table  3.   Correlation degree of Spearman rank correlation coefficient[25]

    $ \left| \rho \right| $ 相关程度
    0.00~0.19 很弱
    0.20~0.39
    0.40~0.59 中等
    0.60~0.79
    0.80~1.0 很强
    下载: 导出CSV

    表  5  实验1中各算法均方根误差

    Table  5.   Root mean square error of each algorithm in experiment 1 m

    算法 2D 3D
    扩展卡尔曼滤波算法 7.99 6.12 9.64 10.06 13.94
    抗差扩展卡尔曼滤波算法 9.40 6.80 5.27 11.60 12.74
    算法1 5.13 4.80 4.19 7.03 8.18
    本文算法 5.11 4.73 4.22 6.96 8.14
    下载: 导出CSV

    表  4  实验1中本文算法滤波类型选择结果

    Table  4.   Selection results of the proposed algorithm filter type in Experiment 1

    滤波类型 采用该滤波的
    总时长/s
    GPS/BDS三维定位
    误差平均值/m
    GPS/BDS三维定位
    误差中位数/m
    自适应滤波 911 4.22 3.88
    抗差滤波 399 23.26 11.36
    下载: 导出CSV

    表  6  实验1中各算法相对于扩展卡尔曼滤波提升率

    Table  6.   Improvement rate of each algorithm relative to extended Kalman filter in Experiment 1 %

    算法 2D 3D
    抗差扩展卡尔曼滤波算法
    卡尔曼滤波算法
    −17.65 −11.11 45.33 −15.27 8.57
    算法1 35.79 21.57 56.54 30.20 41.30
    本文算法 36.05 22.71 56.22 30.82 41.58
    下载: 导出CSV

    表  8  实验2中各算法均方根误差

    Table  8.   Root mean square error of each algorithm in experiment 2 m

    算法 2D 3D
    扩展卡尔曼滤波算法 6.48 4.42 13.66 7.84 15.75
    抗差扩展卡尔曼滤波算法 7.72 4.82 12.60 9.10 15.54
    算法1 6.10 4.20 11.14 7.40 13.37
    本文算法 5.84 4.30 8.73 7.25 11.35
    下载: 导出CSV

    表  7  实验2中本文算法滤波类型选择结果

    Table  7.   The proposed algorithm filter type selection results in experiment 2

    滤波类型 采用该滤波的
    总时长/s
    GPS/BDS三维定位
    误差平均值/m
    GPS/BDS三维
    定位误差中位数/m
    自适应滤波 537 2.97 2.44
    抗差滤波 593 17.82 11.36
    下载: 导出CSV

    表  9  实验2中各算法相对于扩展卡尔曼滤波提升率

    Table  9.   Improvement rate of each algorithm relative to extended Kalman filter in experiment 2 %

    算法 2D 3D
    抗差扩展卡尔曼滤波算法 −19.14 −9.05 7.76 −16.03 1.32
    算法1 5.86 4.98 18.45 5.58 15.08
    本文算法 9.88 2.71 36.09 7.54 27.95
    下载: 导出CSV
  • [1] YANG L, LI Y, WU Y L, et al. An enhanced MEMS-INS/GNSS integrated system with fault detection and exclusion capability for land vehicle navigation in urban areas[J]. GPS Solutions, 2014, 18(4): 593-603. doi: 10.1007/s10291-013-0357-1
    [2] LI B X, CHEN G W, SI Y B, et al. GNSS/INS integration based on machine learning lightGBM model for vehicle navigation[J]. Applied Sciences, 2022, 12(11): 5565. doi: 10.3390/app12115565
    [3] YUE S,CONG L, QIN H L, et al. A robust fusion methodology for MEMS-based land vehicle navigation in GNSS-Challenged environments[J]. IEEE Access, 2020, 8: 44087-44099. doi: 10.1109/ACCESS.2020.2977474
    [4] LYU Z T, GAO Y. An SVM based weight scheme for improving kinematic GNSS positioning accuracy with low-cost GNSS receiver in urban environments[J]. Sensors, 2020, 20(24): 7265. doi: 10.3390/s20247265
    [5] ZHANG G H, XU P H, XU H S, et al. Prediction on the urban GNSS measurement uncertainty based on deep learning networks with long short-term memory[J]. IEEE Sensors Journal, 2021, 21(18): 20563-20577. doi: 10.1109/JSEN.2021.3098006
    [6] SUN R, HSU L T, XUE D B, et al. GPS signal reception classification using adaptive Neuro-Fuzzy inference system[J]. Journal of Navigation, 2019, 72(3): 685-701. doi: 10.1017/S0373463318000899
    [7] WANG J, HAN H Z, MENG X L, et al. Robust wavelet-based inertial sensor error mitigation for tightly coupled GPS/BDS/INS integration during signal outages[J]. Survey Review, 2017, 49(357): 419-427. doi: 10.1080/00396265.2016.1190162
    [8] CHANG G B. Robust Kalman filtering based on Mahalanobis distance as outlier judging criterion[J]. Journal of Geodesy, 2014, 88(4): 391-401. doi: 10.1007/s00190-013-0690-8
    [9] ZHANG Q, NIU X J, SHI C. Impact assessment of various IMU error sources on the relative accuracy of the GNSS/INS systems[J]. IEEE Sensors Journal, 2020, 20(9): 5026-5038. doi: 10.1109/JSEN.2020.2966379
    [10] 杨元喜. 动态系统的抗差Kalman滤波[J]. 解放军测绘学院学报, 1997, 14(2): 79-84.

    YANG Y X. Robust Kalman filter for dynamic systems[J]. Journal of the PLA Institute of Surveying and Mapping, 1997, 14(2): 79-84(in Chinese).
    [11] 王坚, 刘超, 高井祥, 等. 基于抗差EKF的GNSS/INS紧组合算法研究[J]. 武汉大学学报(信息科学版), 2011, 36(5): 596-600.

    WANG J, LIU C, GAO J X, et al. GNSS/INS tightly coupled navigation model based on robust EKF[J]. Geomatics and Information Science of Wuhan University, 2011, 36(5): 596-600(in Chinese).
    [12] JIANG C, ZHANG S B, LI H, et al. Performance evaluation of the filters with adaptive factor and fading factor for GNSS/INS integrated systems[J]. GPS Solutions, 2021, 25(4): 130-141. doi: 10.1007/s10291-021-01165-4
    [13] 吴富梅, 杨元喜. 基于小波阈值消噪自适应滤波的GPS/INS组合导航[J]. 测绘学报, 2007, 36(2): 124-128.

    WU F M, YANG Y X. GPS/INS integrated navigation by adaptive filtering based on wavelet threshold denoising[J]. Acta Geodaetica et Cartographica Sinica, 2007, 36(2): 124-128(in Chinese).
    [14] GAO B B, HU G G, ZHONG Y M, et al. Cubature Kalman filter with both adaptability and robustness for tightly-coupled GNSS/INS integration[J]. IEEE Sensors Journal, 2021, 21(13): 14997-15011. doi: 10.1109/JSEN.2021.3073963
    [15] CHANG G B. Kalman filter with both adaptivity and robustness[J]. Journal of Process Control, 2014, 24(3): 81-87. doi: 10.1016/j.jprocont.2013.12.017
    [16] YANG Y, HE H, XU G. Adaptively robust filtering for kinematic geodetic positioning[J]. Journal of Geodesy, 2001, 75(2-3): 109-116. doi: 10.1007/s001900000157
    [17] 杨元喜, 任夏, 许艳. 自适应抗差滤波理论及应用的主要进展[J]. 导航定位学报, 2013, 1(1): 9-15.

    YANG Y X, REN X, XU Y. Main progress of adaptively robust filter with applications in navigation[J]. Journal of Navigation and Positioning, 2013, 1(1): 9-15(in Chinese).
    [18] 谭兴龙, 王坚, 韩厚增. 支持向量回归辅助的GPS/INS组合导航抗差自适应算法[J]. 测绘学报, 2014, 43(6): 590-597.

    TAN X L, WANG J, HAN H Z. SVR aided adaptive robust filtering algorithm for GPS/INS integrated navigation[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(6): 590-597(in Chinese).
    [19] NING Y P, WANG J, HAN H Z, et al. An optimal radial basis function neural network enhanced adaptive robust Kalman filter for GNSS/INS integrated systems in complex urban areas[J]. Sensors, 2018, 18(9): 3091. doi: 10.3390/s18093091
    [20] 高为广, 陈谷仓. 结合自适应滤波和神经网络的GNSS/INS抗差组合导航算法[J]. 武汉大学学报(信息科学版), 2014, 39(11): 1323-1328.

    GAO W G, CHEN G C. Integrated GNSS/INS navigation algorithms combining adaptive filter with neural network[J]. Geomatics and Information Science of Wuhan University, 2014, 39(11): 1323-1328(in Chinese).
    [21] ZHANG C, ZHAO X B, PANG C L, et al. Improved fault detection method based on robust estimation and sliding window test for INS/GNSS integration[J]. Journal of Navigation, 2020, 73(4): 1-21.
    [22] ATIA M, WASLANDER S. Map-aided adaptive GNSS/IMU sensor fusion scheme for robust urban navigation[J]. Measurement, 2019, 131: 615-627. doi: 10.1016/j.measurement.2018.08.050
    [23] WANG L, GROVES P, ZIEBART M. Multi-constellation GNSS performance evaluation for urban canyons using large virtual reality city models[J]. Journal of Navigation, 2012, 65(3): 459-476. doi: 10.1017/S0373463312000082
    [24] HSU L T, TOKURA H, KUBO N, et al. Multiple faulty GNSS measurement exclusion based on consistency check in urban canyons[J]. IEEE Sensors Journal, 2017, 17(6): 1909-1917. doi: 10.1109/JSEN.2017.2654359
    [25] SUN R, FU L X, WANG G Y, et al. Using dual-polarization GPS antenna with optimized adaptive neuro-fuzzy inference system to improve single point positioning accuracy in urban canyons[J]. Navigation, 2021, 68(1): 41-60. doi: 10.1002/navi.408
    [26] BEST D, ROBERTS D. The upper tail probabilities of Spearman's Rho[J]. Applied Sratistics, 1975, 24(3): 377-379. doi: 10.2307/2347111
  • 加载中
图(13) / 表(9)
计量
  • 文章访问数:  662
  • HTML全文浏览量:  110
  • PDF下载量:  9
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-05-19
  • 录用日期:  2022-07-17
  • 网络出版日期:  2022-08-02
  • 整期出版日期:  2024-04-29

目录

    /

    返回文章
    返回
    常见问答