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

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

南京航空航天大学民航学院，南京 211106

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
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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

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

College of Civil Aviation，Nanjing University of Aeronautics and Astronautics，Nanjing 211106，China

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

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

摘要

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。
- 伪距残差 /
- 新息 /
- 抗差滤波 /
- 自适应滤波 /
- GNSS/IMU组合导航
Abstract:
Algorithms of robust filtering and adaptive filtering are commonly used to improve the positioning accuracy of the navigation system integrating global navigation satellite system (GNSS) and inertial measurement units (IMU). However, the conditions applicable to robust filtering and adaptive filtering are different, and improper use of the filter may reduce the positioning accuracy of the integrated system. To solve this problem, a robust adaptive position algorithm for GNSS/IMU is proposed based on pseudorange residual and innovation. The positioning quality of GNSS is evaluated based on pseudorange residuals. The appropriate filtering algorithm is selected to solve the GNSS/IMU integrated navigation. Innovation and pseudorange residuals are then used to determine whether the IMU kinematic estimation error is greater than the GNSS observation error in long-time low quality GNSS. The robust factor is used based on the determined results. Experimental results show that the positioning accuracy of the proposed algorithm is improved by 36.05%, 22.71%, and 56.22% in the east, north and up directions, respectively, compared with the results from the extended Kalman filter algorithm.
- pseudorange residual /
- innovation /
- robust filtering /
- adaptive filtering /
- GNSS/IMU integrated navigation

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图 1 本文算法框架

Figure 1. The proposed algorithm framework

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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

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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

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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

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图 5 实验1中参考行驶轨迹

Figure 5. Reference driving track in experiment 1

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图 6 行驶轨迹部分场景

Figure 6. Partial scene of driving track

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图 7 实验1中卫星数目

Figure 7. Number of satellites in experiment 1

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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

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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

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图 10 实验2中参考行驶轨迹

Figure 10. Reference driving track in experiment 2

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图 11 实验2中卫星数目

Figure 11. Number of satellites in experiment 2

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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

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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

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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

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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

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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	弱
0.40～0.59	中等
0.60～0.79	强
0.80～1.0	很强

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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	东	北	天	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

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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

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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	东	北	天	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

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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	东	北	天	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

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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

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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	东	北	天	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

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