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

CHEN Shuai; WANG Ning; CHEN Tingkai; YANG Yi; TIAN Jiahe

doi:10.19693/j.issn.1673-3185.02216

Volume 17 Issue 1

Mar. 2022

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Article Navigation > Chinese Journal of Ship Research > 2022 > 17(1): 203-211

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

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

doi: 10.19693/j.issn.1673-3185.02216

CHEN Shuai^1
,,
WANG Ning^{1
,
,},
CHEN Tingkai^1
,,
YANG Yi²,
TIAN Jiahe¹

1.
Marine Electrical Engineering College, Dalian Maritime University, Dalian 116026, China
2.
CRRC Dalian R&D Co., Ltd., Dalian 116085, China

Received Date: 2020-12-07
Rev Recd Date: 2021-03-08

Available Online: 2022-02-26

Publish Date: 2022-03-02

Abstract

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.

Abstract

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.
- confidence check,
- adaptive federated Kalman filter,
- integrated navigation,
- underwater vehicle

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References(20)

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