Seamless Micro-Electro-Mechanical System-Inertial Navigation System/Polarization Compass Navigation Method with Data and Model Dual-Driven Approach
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Published:2024-02-02
Issue:2
Volume:15
Page:237
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ISSN:2072-666X
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Container-title:Micromachines
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language:en
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Short-container-title:Micromachines
Author:
Zhao Huijun1ORCID, Shen Chong1ORCID, Cao Huiliang1ORCID, Chen Xuemei2, Wang Chenguang1, Huang Haoqian3ORCID, Li Jie1
Affiliation:
1. The State Key Laboratory of Dynamic Measurement Technology, The School of Instrument and Electronics, North University of China, Taiyuan 030051, China 2. The Intelligent Vehicle Research Center, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China 3. The College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Abstract
The integration of micro-electro-mechanical system–inertial navigation systems (MEMS-INSs) with other autonomous navigation sensors, such as polarization compasses (PCs) and geomagnetic compasses, has been widely used to improve the navigation accuracy and reliability of vehicles in Internet of Things (IoT) applications. However, a MEMS-INS/PC integrated navigation system suffers from cumulative errors and time-varying measurement noise covariance in unknown, complex occlusion, and dynamic environments. To overcome these problems and improve the integrated navigation system’s performance, a dual data- and model-driven MEMS-INS/PC seamless navigation method is proposed. This system uses a nonlinear autoregressive neural network (NARX) based on the Gauss–Newton Bayesian regularization training algorithm to model the relationship between the MEMS-INS outputs composed of the specific force and angular velocity data and the PC heading’s angular increment, and to fit the integrated navigation system’s dynamic characteristics, thus realizing data-driven operation. In the model-driven part, a nonlinear MEMS-INS/PC loosely coupled navigation model is established, the variational Bayesian method is used to estimate the time-varying measurement noise covariance, and the cubature Kalman filter method is then used to solve the nonlinear problem in the model. The robustness and effectiveness of the proposed method are verified experimentally. The experimental results show that the proposed method can provide high-precision heading information stably in complex, occluded, and dynamic environments.
Funder
National Natural Science Foundation of China Excellent Youth Foundation of Shanxi Province Foundation of Science and Technology on Electro-Optical Information Security Control Laboratory Shanxi Province Key Laboratory of Quantum Sensing and Precision Measurement 1331 Project of Shanxi Province
Subject
Electrical and Electronic Engineering,Mechanical Engineering,Control and Systems Engineering
Reference34 articles.
1. Extended Kalman/UFIR Filters for UWB-Based Indoor Robot Localization Under Time-Varying Colored Measurement Noise;Xu;IEEE Internet Things J.,2023 2. Tightly Coupled Integration of INS and UWB Using Fixed-Lag Extended UFIR Smoothing for Quadrotor Localization;Xu;IEEE Internet Things J.,2021 3. Hu, G., Xu, L., Gao, B., Chang, L., and Zhong, Y. (2023). Robust Unscented Kalman Filter-Based Decentralized Multisensor Information Fusion for INS/GNSS/CNS Integration in Hypersonic Vehicle Navigation. IEEE Trans. Instrum. Meas., 72. 4. Yan, Z., Chen, X., Tang, X., and Zhu, X. (2022). Design and Performance Evaluation of the Improved INS-Assisted Vector Tracking for the Multipath in Urban Canyons. IEEE Trans. Instrum. Meas., 71. 5. Zhang, X., Tang, J., Cao, H., Wang, C., Shen, C., and Liu, J. (2024). Cascaded Speech Separation Denoising and Dereverberation Using Attention and TCN-WPE Networks for Speech Devices. IEEE Internet Things J., in press.
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