Gaussian process regression-based quaternion unscented Kalman robust filter for integrated SINS/GNSS

doi:10.23919/JSEE.2022.000105

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (5): 1079-1088.doi: 10.23919/JSEE.2022.000105

• ELECTRONICS TECHNOLOGY • Previous Articles Next Articles

Gaussian process regression-based quaternion unscented Kalman robust filter for integrated SINS/GNSS

Xu LYU^1,²(), Baiqing HU¹(), Yongbin DAI³(), Mingfang SUN^4,*(), Yi LIU¹(), Duanyang GAO¹()

¹ College of Electrical Engineering, Naval University of Engineering, Wuhan 430033, China
² Beijing Huahang Radio Measurement Research Institute, Beijing 100000, China
³ School of Electrical Engineering, Liaoning University of Technology, Jinzhou 121001, China
⁴ School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China

Received:2020-12-14 Online:2022-10-27 Published:2022-10-27
Contact: Mingfang SUN E-mail:lvclay@163.com;hubaiqing2005@163.com;dyb16@163.com;sunmf125@163.com;565175757@qq.com;gdyhgn@163.com
About author:|LYU Xu was born in 1990. He received his B.S. and M.S. degrees in Control Theory and Control Engineering from the Department of Electrical Engineering, Liaoning University of Technology, Jinzhou, China, in 2014 and 2019, respectively. He received his Ph.D. degree in navigation, guidance and control from the Department of Navigation Engineering, Naval University of Engineering, Wuhan, China, in 2022. He now works in Beijing Huahang Radio Measurement Research Institute. His scienti?c interests include inertial navigation systems, integrated navigation, and predictive control. E-mail: lvclay@163.com||HU Baiqing was born in 1964. He received his B.S. and M.S. degrees in navigation engineering from Naval Academy of Engineering, China, in 1983 and 1986, respectively, and the Ph.D. degree in precision instruments and mechanology from Tsinghua University, Beijing, China, in 2008. He is currently a professor, a doctoral tutor, and the Director of the Department of Navigation Engineering with the Naval University of Engineering. His scienti?c interests include inertial navigation, guidance, and control. E-mail: hubaiqing2005@163.com||DAI Yongbin was born in 1972. He received his B.S. and M.S. degrees in Control Theory and Control Engineering from Liaoning University of Technology, Jinzhou, China, in 1996 and 2003, respectively, and his Ph.D. degree in Control Science and Engineering from the University of Science and Technology Beijing, China, in 2010. He is currently a professor, a doctoral tutor, and the Director of the Department of College of Software with the Liaoning University of Technology. His scienti?c interests include Non-linear system control and predictive control. E-mail: dyb16@163.com||SUN Mingfang was born in 1981. He received his B.S and M.S degrees in information and communication engineering, Harbin Institute of Technology, China, in 2004 and 2006, respectively. He has been working at Institute of Beijing Huahang Radio Measurement since 2006. He is currently a Ph.D. candidate in information and communication, Harbin Institute of Technology in 2017. His research interests include radar signal detection and tracking. E-mail: sunmf125@163.com||LIU Yi was born in 1992. He received his B.S. and M.S. degrees in surveying and mapping engineering from the Department of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, China, in 2016 and 2019, respectively. He is currently a Ph.D. candidate in GNSS precise point positioning from the Department of Navigation Engineering, Naval University of Engineering, Wuhan, China, in 2019. His research interest is multi-sensor fusion navigation. E-mail: 565175757@qq.com||GAO Duanyang was born in 1995. He received his B.S. and M.S. degrees in navigation from the Department of Navigation, Naval University of Engineering, Wuhan, China, in 2014 and 2019, respectively. He is currently a Ph.D. candidate in navigation, guidance and control from the Department of Navigation Engineering, Naval University of Engineering, Wuhan, China, in 2019. His scienti?c interests include inertial navigation systems, integrated navigation, and predictive control. E-mail: gdyhgn@163.com
Supported by:
This work was supported by the National Natural Science Foundation of China (61873275; 61703419; 425317829).

Abstract

Abstract:

High-precision filtering estimation is one of the key techniques for strapdown inertial navigation system/global navigation satellite system (SINS/GNSS) integrated navigation system, and its estimation plays an important role in the performance evaluation of the navigation system. Traditional filter estimation methods usually assume that the measurement noise conforms to the Gaussian distribution, without considering the influence of the pollution introduced by the GNSS signal, which is susceptible to external interference. To address this problem, a high-precision filter estimation method using Gaussian process regression (GPR) is proposed to enhance the prediction and estimation capability of the unscented quaternion estimator (USQUE) to improve the navigation accuracy. Based on the advantage of the GPR machine learning function, the estimation performance of the sliding window for model training is measured. This method estimates the output of the observation information source through the measurement window and realizes the robust measurement update of the filter. The combination of GPR and the USQUE algorithm establishes a robust mechanism framework, which enhances the robustness and stability of traditional methods. The results of the trajectory simulation experiment and SINS/GNSS car-mounted tests indicate that the strategy has strong robustness and high estimation accuracy, which demonstrates the effectiveness of the proposed method.

Key words: integrated navigation, Gaussian process regression (GPR), quaternion, Kalman filter, robustness

Xu LYU, Baiqing HU, Yongbin DAI, Mingfang SUN, Yi LIU, Duanyang GAO. Gaussian process regression-based quaternion unscented Kalman robust filter for integrated SINS/GNSS[J]. Journal of Systems Engineering and Electronics, 2022, 33(5): 1079-1088.

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Algorithm	Mean		Mean variance
Algorithm	USQUE	GPR-USQUE	USQUE	GPR-USQUE
Pitch	1.0394	0.1963	1.9027	0.0858
Roll	0.9617	0.3087	1.7816	0.1711
Yaw	30.9339	1.5175	1228.1	16.0466

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Gaussian process regression-based quaternion unscented Kalman robust filter for integrated SINS/GNSS

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