An improved Kalman filter algorithm for tightly GNSS/INS integrated navigation system
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Published:2023
Issue:1
Volume:21
Page:963-983
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ISSN:1551-0018
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Container-title:Mathematical Biosciences and Engineering
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language:
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Short-container-title:MBE
Author:
Yuan Yuelin1, Li Fei2, Chen Jialiang2, Wang Yu3, Liu Kai2
Affiliation:
1. School of Electronic Science, National University of Defense Technology, Changsha 410005, China 2. School of Transportation and Logistics, Dalian University of Technology, Dalian 116024, China 3. School of Traffic and Transportation Engineering, Dalian Jiaotong University, Dalian 116028, China
Abstract
<abstract><p>The Kalman filter based on singular value decomposition (SVD) can sufficiently reduce the accumulation of rounding errors and is widely used in various applications with numerical calculations. However, in order to improve the filtering performance and adaptability in a tightly GNSS/INS (Global Navigation Satellite System and Inertial Navigation System) integrated navigation system, we propose an improved robust method to satisfy the requirements. To solve the issue of large fluctuations in GNSS signals faced by the conventional method that uses a fixed noise covariance, the proposed method constructs a correction variable through the innovation and the new matrix which is obtained by performing SVD on the original matrix, dynamically correcting the noise covariance and has better robustness. In addition, the derived SVD form of the information filter (IF) extends its application. The proposed method has higher positioning accuracy and can be better applied to tightly coupled GNSS/INS navigation simulations and physical experiments. The experimental results show that, compared with the traditional Kalman algorithm based on SVD, the proposed algorithm*s maximum error is reduced by 45.77%. Compared with the traditional IF algorithm, the root mean squared error of the proposed IF algorithm in the form of SVD is also reduced by 4.7%.</p></abstract>
Publisher
American Institute of Mathematical Sciences (AIMS)
Subject
Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine
Reference36 articles.
1. Q. Wang, X. Hu, An improve differential algorithm for GPS static positioning, in 2008 IEEE International Symposium on Knowledge Acquisition and Modeling Workshop, (2008), 58–61. https://doi.org/10.1109/KAMW.2008.4810424 2. M. Shao, X. Sui, Study on differential GPS positioning methods, in 2015 International Conference on Computer Science and Mechanical Automation (CSMA), (2015), 223–225. https://doi.org/10.1109/CSMA.2015.51 3. X. Gan, B. Yu, Research on multimodal SBAS technology supporting precision single point positioning, in 2015 International Conference on Computers, Communications, and Systems (ICCCS), (2015), 131–135. https://doi.org/10.1109/CCOMS.2015.7562887 4. Y. Xu, K. Wang, C. Yang, Z. Li, F. Zhou, D. Liu, GNSS/INS/OD/NHC Adaptive Integrated Navigation Method Considering the Vehicle Motion State, IEEE Sensors J., 23 (2023), 13511–13523. https://doi.org/10.1109/JSEN.2023.3272507 5. G. Chen, J. Wang, H. Hu, An integrated GNSS/INS/DR positioning strategy considering nonholonomic constraints for intelligent vehicle, in 2022 6th CAA International Conference on Vehicular Control and Intelligence (CVCI), (2022), 1–6. https://doi.org/10.1109/CVCI56766.2022.9964661
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