Predicting the Noise Covariance With a Multitask Learning Model for Kalman Filter-Based GNSS/INS Integrated Navigation

doi:10.1109/TIM.2020.3024357

	Predicting the Noise Covariance With a Multitask Learning Model for Kalman Filter-Based GNSS/INS Integrated Navigation
	Wu, Fan 1; Luo, Haiyong 2; Jia, Hongwei 1; Zhao, Fang 1; Xiao, Yimin 1; Gao, Xile 2
	2021
发表期刊	IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
ISSN	0018-9456
卷号	70 页码:13
摘要	In the recent years, the availability of accurate vehicle position becomes more urgent. The global navigation satellite systems/inertial navigation system (GNSS/INS) is the most used integrated navigation scheme for land vehicles, which utilizes the Kalman filter (KF) to optimally fuse GNSS measurement and INS prediction for accurate and robust localization. However, the uncertainty of the process noise covariance and the measurement noise covariance has a significant impact on Kalman filtering performance. Traditional KF-based integrated navigation methods configure the process noise covariance and measurement noise covariance with predefined constants, which cannot adaptively characterize the various and dynamic environments, and obtain accurate and continuous positioning results under complex environments. To obtain accurate and robust localization results under various complex and dynamic environments, in this article, we propose a novel noise covariance estimation algorithm for the GNSS/INS-integrated navigation using multitask learning model, which can simultaneously estimate the process noise covariance and measurement noise covariance for the Kle. The predicted multiplication factors are used to dynamically scale process noise covariance matrix and measurement noise covariance matrix respectively according to the inputs of raw inertial measurement. Extensive experiments are conducted on our collected practical road data set under three typical complex urban scenarios, such as, avenues, viaducts, and tunnels. Experimental results demonstrate that compared with the traditional KF-based integrated navigation algorithm with predefined fixed settings, our proposed method reduces 77.13% positioning error.
关键词	Adaptive integrated navigation deep learning denoising autoencoder (DAE) Kalman filter (KF) measurement noise process noise
DOI	10.1109/TIM.2020.3024357
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program[2018YFB0505200] ; Action Plan Project of the Beijing University of Posts and Telecommunications ; Fundamental Research Funds for the Central Universities[2019XD-A06] ; Fundamental Research Funds for the Central Universities[2019PTB-011] ; Special Project for Youth Research and Innovation, Beijing University of Posts and Telecommunications ; National Natural Science Foundation of China[61872046] ; National Natural Science Foundation of China[61761038] ; Joint Research Fund for the Beijing Natural Science Foundation and Haidian Original Innovation[L192004] ; Key Research and Development Project from Hebei Province[19210404D] ; Key Research and Development Project from Hebei Province[20313701D] ; Science and Technology Plan Project of Inner Mongolia Autonomous Region[2019GG328] ; Open Project of the Beijing Key Laboratory of Mobile Computing and Pervasive Device
WOS研究方向	Engineering ; Instruments & Instrumentation
WOS类目	Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS记录号	WOS:000597200000002
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：39[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/16560
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Luo, Haiyong
作者单位	1.Beijing Univ Posts & Telecommun, Sch Software Engn, Beijing 100876, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Res Ctr Ubiquitous Comp Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Wu, Fan,Luo, Haiyong,Jia, Hongwei,et al. Predicting the Noise Covariance With a Multitask Learning Model for Kalman Filter-Based GNSS/INS Integrated Navigation[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,2021,70:13.
APA	Wu, Fan,Luo, Haiyong,Jia, Hongwei,Zhao, Fang,Xiao, Yimin,&Gao, Xile.(2021).Predicting the Noise Covariance With a Multitask Learning Model for Kalman Filter-Based GNSS/INS Integrated Navigation.IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,70,13.
MLA	Wu, Fan,et al."Predicting the Noise Covariance With a Multitask Learning Model for Kalman Filter-Based GNSS/INS Integrated Navigation".IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 70(2021):13.