Optimizing GNSS/INS Integrated Navigation: A Deep Learning Approach for Error Compensation

doi:10.1109/LSP.2024.3484292

	Optimizing GNSS/INS Integrated Navigation: A Deep Learning Approach for Error Compensation
	Wu, Fan 1; Luo, Haiyong 2; Zhao, Fang 1; Wei, Liangrui 1; Zhou, Bo 3
	2024
发表期刊	IEEE SIGNAL PROCESSING LETTERS
ISSN	1070-9908
卷号	31 页码:3104-3108
摘要	This letter addresses challenges stemming from sensor manufacturing processes and technological constraints, such as nonlinear stochastic noise, leading to rapid INS positioning error divergence. To enhance the performance of GNSS/INS (Global Navigation Satellite System/Integrated Navigation Systems) integrated navigation methods, we propose an AI-based adaptive error compensation method. We introduce deep learning to correct INS computational errors, leveraging its precision modeling without strict noise assumptions. Our approach integrates filtering methods and deep learning approaches, avoiding the uncontrollable introduction of positioning errors inherent in end-to-end models' black-box mode. We design a novel deep model structure to improve generalization while reducing parameters and computational complexity. Validation is conducted using a vehicular navigation data acquisition platform, simulating scenarios of GNSS signal loss. Experimental results demonstrate a 77.70% improvement in recognition rates across different road segments compared to traditional methods based on Extended Kalman Filter, indicating significant practical value.
关键词	Global navigation satellite systems inertial navigation system probSparse self-attention probSparse self-attention urban canyon urban canyon urban canyon
DOI	10.1109/LSP.2024.3484292
收录类别	SCI
语种	英语
资助项目	Strategic Priority Research Program of Chinese Academy of Sciences[XDA28040500] ; National Natural Science Foundation of China[62261042] ; Key Research Projects of the Joint Research Fund for Beijing Natural Science Foundation ; Fengtai Rail Transit Frontier Research Joint Fund[L221003] ; Beijing Natural Science Foundation[4232035] ; Beijing Natural Science Foundation[4222034] ; BUPT Excellent Ph.D. Students Foundation[CX2022131]
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:001358187900003
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/41189
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 3.Yibin Tinno Commun Co Ltd, Sci & Innovat Ctr, Guoxing Ave,Lingang Econ Dev Zone, Yibin 644000, Sichuan, Peoples R China
推荐引用方式 GB/T 7714	Wu, Fan,Luo, Haiyong,Zhao, Fang,et al. Optimizing GNSS/INS Integrated Navigation: A Deep Learning Approach for Error Compensation[J]. IEEE SIGNAL PROCESSING LETTERS,2024,31:3104-3108.
APA	Wu, Fan,Luo, Haiyong,Zhao, Fang,Wei, Liangrui,&Zhou, Bo.(2024).Optimizing GNSS/INS Integrated Navigation: A Deep Learning Approach for Error Compensation.IEEE SIGNAL PROCESSING LETTERS,31,3104-3108.
MLA	Wu, Fan,et al."Optimizing GNSS/INS Integrated Navigation: A Deep Learning Approach for Error Compensation".IEEE SIGNAL PROCESSING LETTERS 31(2024):3104-3108.