RL-AKF: An Adaptive Kalman Filter Navigation Algorithm Based on Reinforcement Learning for Ground Vehicles

doi:10.3390/rs12111704

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	RL-AKF: An Adaptive Kalman Filter Navigation Algorithm Based on Reinforcement Learning for Ground Vehicles
	Gao, Xile 1; Luo, Haiyong 1; Ning, Bokun 2; Zhao, Fang 2; Bao, Linfeng 1; Gong, Yilin 2; Xiao, Yimin 2; Jiang, Jinguang 3
刊名	REMOTE SENSING
	2020-06-01
卷号	12 期号:11 页码:25
关键词	integrated navigation Kalman filter process noise covariance estimation reinforcement learning deep deterministic policy gradient
DOI	10.3390/rs12111704
英文摘要	Kalman filter is a commonly used method in the Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) integrated navigation system, in which the process noise covariance matrix has a significant influence on the positioning accuracy and sometimes even causes the filter to diverge when using the process noise covariance matrix with large errors. Though many studies have been done on process noise covariance estimation, the ability of the existing methods to adapt to dynamic and complex environments is still weak. To obtain accurate and robust localization results under various complex and dynamic environments, we propose an adaptive Kalman filter navigation algorithm (which is simply called RL-AKF), which can adaptively estimate the process noise covariance matrix using a reinforcement learning approach. By taking the integrated navigation system as the environment, and the opposite of the current positioning error as the reward, the adaptive Kalman filter navigation algorithm uses the deep deterministic policy gradient to obtain the most optimal process noise covariance matrix estimation from the continuous action space. Extensive experimental results show that our proposed algorithm can accurately estimate the process noise covariance matrix, which is robust under different data collection times, different GNSS outage time periods, and using different integration navigation fusion schemes. The RL-AKF achieves an average positioning error of 0.6517 m within 10 s GNSS outage for GNSS/INS integrated navigation system and 14.9426 m and 15.3380 m within 300 s GNSS outage for the GNSS/INS/Odometer (ODO) and the GNSS/INS/Non-Holonomic Constraint (NHC) integrated navigation systems, respectively.
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] ; Special Project for Youth Research and Innovation, Beijing University of Posts and Telecommunications ; Fundamental Research Funds for the Central Universities[2019PTB-011] ; National Natural Science Foundation of China[61872046] ; National Natural Science Foundation of China[61761038] ; Joint Research Fund for Beijing Natural Science Foundation[L192004] ; Haidian Original Innovation[L192004] ; Key Research and Development Project from Hebei Province[19210404D] ; Science and Technology Plan Project of Inner Mongolia Autonomous Regio[2019GG328] ; Open Project of the Beijing Key Laboratory of Mobile Computing and Pervasive Device
WOS研究方向	Remote Sensing
语种	英语
出版者	MDPI
WOS记录号	WOS:000543397000009
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/15121]
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.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100190, Peoples R China 2.Beijing Univ Posts & Telecommun, Sch Software Engn, Beijing 100876, Peoples R China 3.Wuhan Univ, GNSS Res Ctr, Wuhan 430072, Peoples R China
推荐引用方式 GB/T 7714	Gao, Xile,Luo, Haiyong,Ning, Bokun,et al. RL-AKF: An Adaptive Kalman Filter Navigation Algorithm Based on Reinforcement Learning for Ground Vehicles[J]. REMOTE SENSING,2020,12(11):25.
APA	Gao, Xile.,Luo, Haiyong.,Ning, Bokun.,Zhao, Fang.,Bao, Linfeng.,...&Jiang, Jinguang.(2020).RL-AKF: An Adaptive Kalman Filter Navigation Algorithm Based on Reinforcement Learning for Ground Vehicles.REMOTE SENSING,12(11),25.
MLA	Gao, Xile,et al."RL-AKF: An Adaptive Kalman Filter Navigation Algorithm Based on Reinforcement Learning for Ground Vehicles".REMOTE SENSING 12.11(2020):25.