Volume 17 Issue 1
Mar.  2022
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CHEN S, WANG N, CHEN T K, et al. Confidence check-adaptive federated Kalman filter and its application in underwater vehicle integrated navigation[J]. Chinese Journal of Ship Research, 2022, 17(1): 203–211, 220 doi: 10.19693/j.issn.1673-3185.02216
Citation: CHEN S, WANG N, CHEN T K, et al. Confidence check-adaptive federated Kalman filter and its application in underwater vehicle integrated navigation[J]. Chinese Journal of Ship Research, 2022, 17(1): 203–211, 220 doi: 10.19693/j.issn.1673-3185.02216

Confidence check-adaptive federated Kalman filter and its application in underwater vehicle integrated navigation

doi: 10.19693/j.issn.1673-3185.02216
  • Received Date: 2020-12-07
  • Rev Recd Date: 2021-03-08
  • Available Online: 2022-02-26
  • Publish Date: 2022-03-02
    © 2022 The Authors. Published by Editorial Office of Chinese Journal of Ship Research. Creative Commons License
    This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  •   Objectives  In order to solve the problem of the reduced accuracy of integrated navigation when a carrier is disturbed, a confidence check-adaptive federated Kalman filter (CC-AFKF) framework is proposed.  Methods  First, the electronic compass (EC), global positioning system (GPS) and inertial navigation system (INS) are combined. Second, a confidence check model is constructed to effectively filter out low-confidence measurements in the INS/GPS and INS/EC subsystems, and ensure the accuracy of the measured value. Finally, an adaptive adjustment factor strategy for the INS/GPS and INS/EC systems is proposed to effectively adjust system noise covariance during the update process.  Results  A large number of related tests are carried out through an underwater vehicle equipped with INS/GPS/EC integrated navigation systems. The test results show that the CC-AFKF algorithm proposed in this paper can improve the integrated accuracy of position and velocity by at least 29% compared with typical KF and FKF algorithms.  Conclusions  The results of this study can provide corresponding directions and ideas for research on loosely coupled integrated navigation systems.
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