Towards Predicting the Measurement Noise Covariance with a Transformer and Residual Denoising Autoencoder for GNSS/INS Tightly-Coupled Integrated Navigation

doi:10.3390/rs14071691

	Towards Predicting the Measurement Noise Covariance with a Transformer and Residual Denoising Autoencoder for GNSS/INS Tightly-Coupled Integrated Navigation
	Xu, Hongfu 1; Luo, Haiyong 2; Wu, Zijian 1; Wu, Fan 1; Bao, Linfeng 2; Zhao, Fang 1
	2022-04-01
发表期刊	REMOTE SENSING
卷号	14 期号:7 页码:22
摘要	The tightly coupled navigation system is commonly used in UAV products and land vehicles. It adopts the Kalman filter to combine raw satellite observations, including the pseudorange, pseudorange rate and Doppler frequency, with the inertial measurements to achieve high navigational accuracy in GNSS-challenged environments. The accurate estimation of measurement noise covariance can ensure the quick convergence of the Kalman filter and the accuracy of the navigation results. Existing tightly coupled integrated navigation systems employ either constant noise covariance or simple noise covariance updating methods, which cannot accurately reflect the dynamic measurement noises. In this article, we propose an adaptive measurement noise estimation algorithm using a transformer and residual denoising autoencoder (RDAE), which can dynamically estimate the covariance of measurement noise. The residual module is used to solve the gradient degradation problem. The DAE is adopted to learn the essential characteristics from the noisy ephemeris data. By introducing the attention mechanism, the transformer can effectively learn the time and space dependency of long-term ephemeris data, and thus dynamically adjusts the noise covariance with the predicted factors. Extensive experimental results demonstrate that our method can achieve sub-meter positioning accuracy in the outdoor open environment. In a GNSS-degraded environment, our proposed method can still obtain about 3 m positioning accuracy. Another test on a new dataset also confirms that our proposed method has reasonable robustness and adaptability.
关键词	tightly coupled integrated navigation measurement noise estimation transformer adaptive Kalman filtering
DOI	10.3390/rs14071691
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program[2018YFB0505200] ; Beijing University of Posts and Telecommunications - Fundamental Research Funds for the Central Universities[2019XD-A06] ; National Natural Science Foundation of China[61872046] ; National Natural Science Foundation of China[62002026] ; Joint Research Fund for Beijing Natural Science Foundation[L192004] ; Haidian Original Innovation[L192004] ; Beijing Natural Science Foundation[4212024] ; Beijing Natural Science Foundation[4222034] ; Key Research and Development Project from Hebei Province[19210404D] ; Key Research and Development Project from Hebei Province[21310102D] ; Science and Technology Plan Project of Inner Mongolia Autonomous Region[2019GG328] ; Open Project of the Beijing Key Laboratory of Mobile Computing and Pervasive Devices
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000780509000001
出版者	MDPI
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/18897
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 Comp Sci, Natl Demonstrat Software Inst, Beijing 100876, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Res Ctr Ubiquitous Comp Syst, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Xu, Hongfu,Luo, Haiyong,Wu, Zijian,et al. Towards Predicting the Measurement Noise Covariance with a Transformer and Residual Denoising Autoencoder for GNSS/INS Tightly-Coupled Integrated Navigation[J]. REMOTE SENSING,2022,14(7):22.
APA	Xu, Hongfu,Luo, Haiyong,Wu, Zijian,Wu, Fan,Bao, Linfeng,&Zhao, Fang.(2022).Towards Predicting the Measurement Noise Covariance with a Transformer and Residual Denoising Autoencoder for GNSS/INS Tightly-Coupled Integrated Navigation.REMOTE SENSING,14(7),22.
MLA	Xu, Hongfu,et al."Towards Predicting the Measurement Noise Covariance with a Transformer and Residual Denoising Autoencoder for GNSS/INS Tightly-Coupled Integrated Navigation".REMOTE SENSING 14.7(2022):22.