Information Fusion Algorithm of GNSS/SINS Integrated Navigation System Based on VB-AUKF

SUN Yumei; PAN Xinlong; WANG Meichun

doi:10.15892/j.cnki.djzdxb.2023.03.001

Journal of Projectiles, Rockets, Missiles and Guidance >

2023 , Vol. 43 >Issue 3: 1 - 8

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2023.03.001

Information Fusion Algorithm of GNSS/SINS Integrated Navigation System Based on VB-AUKF

SUN Yumei ¹ ,
PAN Xinlong ² ,
WANG Meichun ¹

Expand

¹ School of Computer Science,Weifang University of Science and Technology,Weifang 262700,Shandong,China
² Naval Aviation University,Yantai 264001,Shandong,China

Received date: 2023-02-08

Online published: 2025-02-07

Fold

Abstract

This paper firstly proposes a variational Bayesian adaptive UKF algorithm (VB-AUKF) for this integrated navigation system, aiming at the defect that the standard UKF algorithm of GNSS/SINS integrated navigation system cannot adaptively adjust the root mean square (RMS) of measurement noise. Then, an iterative estimation method for RMS of measurement noise is proposed in VB-UKF algorithm. Finally, a lot of theoretical simulation experiments are carried out for the three parameters that affect the accuracy of the variable Bayesian estimation and the integrated navigation system based on VB-AUKF algorithm. The experimental results show that the moderating factor of VB-AUKF algorithm is the key factor to determine the estimation accuracy of the RMS of the measurement noise, while the other two parameters only affect the initial value of the RMS estimation of the measurement noise; the VB-AUKF algorithm can adaptively estimate this RMS when the RMS of measurement noise changes; and compared with the standard UKF algorithm, the VB-AUKF algorithm can improve the accuracy of position and velocity by about 12% and 51%, and greatly improve the accuracy of attitude estimation.

Key words： variational Bayesian; adjustment factor; integrated navigation system; unscented Kalman filter

Cite this article

SUN Yumei , PAN Xinlong , WANG Meichun . Information Fusion Algorithm of GNSS/SINS Integrated Navigation System Based on VB-AUKF[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2023 , 43(3) : 1 -8 . DOI: 10.15892/j.cnki.djzdxb.2023.03.001

[an error occurred while processing this directive]

References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	ZHANG S, ZOU Z. Algorithm simulation of ship dynamic positioning using adaptive fading memory filter[J]. Journal of Ship Mechanics, 2017, 21(12): 1497-1506.

[2]	ULLAH I, SHEN Y, SY X. A localization based on unscented Kalman filter and particle filter localization algorithms[J]. IEEE Access, 2020, 8: 2232-2245.

[3]

沈凯, 刘庭欣, 左思琪, 等. 复杂城市环境下GNSS/INS组合导航可观测度分析及鲁棒滤波方法[J]. 仪器仪表学报, 2020, 41(9): 252-261.

SHEN

, LIU

T X

, ZUO

S Q

, et al. Observability analysis and robust fusion algorithms of GNSS/INS integrated navigation in complex urban environment[J]. Chinese Journal of Scientific Instrument, 2020, 41(9): 252-261.

[4]	潘泉, 胡玉梅, 兰华, 等. 信息融合理论研究进展: 基于变分贝叶斯的联合优化[J]. 自动化学报, 2019, 45(7): 1207-1223. PAN Q, HU Y M, LAN H, et al. Information fusion progress: joint optimization based on variational Bayesian theory[J]. Acta Automatica Sinica, 2019, 45(7): 1207-1223.

[5]	潘泉, 胡玉梅, 马季容. 基于变分贝叶斯联合优化的情报监视与侦察[J]. 指挥信息系统与技术, 2020, 11(2): 1-8. PAN Q, HU Y M, MA J R. Intelligence, surveillance and reconnaissance based on variational Bayesian joint optimization[J]. Command Information System and Technology, 2020, 11(2): 1-8.

[6]	闫文旭, 兰华, 王增福. 基于变分贝叶斯的星载雷达非线性滤波[J]. 航空学报, 2020, 41(S2): 724395-1-724395-9. YAN W X, LAN H, WANG Z F. Nonlinear filtering for spaceborne radars based on variational Bayes[J]. Acta Aeonautica et Asronautica Sinica, 2020, 41(S2): 724395-1-724395-9.

[7]	陈小波, 陈玲. 定位噪声统计特性未知的变分贝叶斯协同目标跟踪[J]. 吉林大学学报(工学版), 2023, 53(4): 1030-1039. CHEN X B, CHEN L. Variational Bayesian cooperative target tracking with unknown localization noise statistics[J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(4): 1030-1039.

[8]

戴卿, 隋立芬, 田源, 等. 变分优化的高斯混合滤波及其在导航中的应用[J]. 武汉大学学报(信息科学版), 2019, 44(5): 699-705.

DAI

, SUI

L F

, TIAN

, et al. Gaussian mixture filter based on variational Bayesian learning optimization and its application to integrated navigation[J]. Geomatics and Information Science of Wuhan University, 2019, 44(5): 699-705.

[9]	沈忱, 徐定杰, 沈锋, 等. GPS/SINS组合导航的变分贝叶斯自适应卡尔曼滤波[J]. 哈尔滨工业大学学报, 2014, 46(5): 59-65. SHEN C, XU D J, SHEN F, et al. Variational Bayesian adaptive Kalman filtering for GPS / INS integrated navigation[J]. Journal of Harbin Institute of Technology, 2014, 46(5): 59-65.

[10]	李博琳, 徐彬, 王凤聚, 等. 基于变分贝叶斯原理的SINS/GPS组合导航VB-CKF滤波算法[J]. 科学技术与工程, 2019, 19(28): 396-400. LI B L, XU B, WANG F J, et al. VB-CKF for SINS/GPS integrated navigation system based on variational Bayesian theory[J]. Science Technology and Engineering, 2019, 19(28): 396-400.

[11]	赵俊波, 葛锡云, 成月, 等. 基于改进变分贝叶斯滤波的SINS/DVL/LBL组合导航技术[J]. 水下无人系统学报, 2021, 29(1): 54-59. ZHAO J B, GE X Y, CHENG Y, et al. SINS/DVL/LBL integrated navigation technology based on improved variational Bayesian filtering[J]. Journal of Unmanned Undersea Systems, 2021, 29(1): 54-59.

[12]	DAVARI N, GHOLAMI A. Variational Bayesian adaptive Kalman filter for asynchronous multirate multi-sensor integrated navigation system[J]. Ocean Engineering, 2019, 17(4): 108-116.

[13]	胡淼淼, 敬忠良, 董鹏, 等. 基于T分布变分贝叶斯滤波的SINS/GPS组合导航[J]. 浙江大学学报(工学版), 2018, 52(8): 1482-1488. HU M M, JING Z L, DONG P, et al. Variational Bayesian filtering based on student-T distribution for SINS/GPS integrated navigation[J]. Journal of Zhejiang University (Engineering Science), 2018, 52(8): 1482-1488.

[14]	胡淼淼. 基于变分贝叶斯滤波的SINS/GPS组合导航[D]. 上海: 上海交通大学, 2018. HU M M. SINS/GPS Integrated navigation based on variational Bayesian filtering method[D]. Shanghai: Shanghai Jiao Tong University, 2018.

[15]	VRETTAS M D, CORNFORD D, OPPER M. Estimating parameters in stochastic systems: a variational Bayesian approach[J]. Physica D Nonlinear Phenomena, 2011, 24(23): 1877-1900.

[16]	LIM K L, WANG H. MAP Approximation to the variational Bayes Gaussian mixture model and application[J]. Soft Computing, 2017, (3): 1-13.

[17]	林雪原, 刘丽丽, 董云云, 等. 改进的GNSS/SINS组合导航系统自适应滤波算法[J]. 武汉大学学报(信息科学版), 2023, 48(1): 54-57. LIN X Y, LIU L L, DONG Y Y, et al. Improved adaptive filtering algorithm for GNSS/SINS integrated navigation system[J]. Geomatics and Information Science of Wuhan University, 2023, 48(1): 127-134.

Options

Outlines