A new direct filtering approach to INS/GNSS integration

Hu, G, Wang, W, Zhong, Y, Gao, B and Gu, C 2018, 'A new direct filtering approach to INS/GNSS integration', Aerospace Science and Technology, vol. 77, pp. 755-764.

Document type: Journal Article
Collection: Journal Articles

Title A new direct filtering approach to INS/GNSS integration
Author(s) Hu, G
Wang, W
Zhong, Y
Gao, B
Gu, C
Year 2018
Journal name Aerospace Science and Technology
Volume number 77
Start page 755
End page 764
Total pages 10
Publisher Elsevier Masson
Abstract This paper presents a novel direct filtering approach to INS/GNSS (Inertial Navigation System / Global Navigation Satellite System) integration. This approach establishes a kinematic model for INS/GNSS integration by combining inertial navigation equations and IMU (Inertial Measurement Unit) error equations. Subsequently, a refined strong tracking unscented Kalman filter (RSTUKF) is developed to enhance the UKF robustness against kinematic model error. This RSTUKF adopts the strategy of assumption test to identify kinematic model error. Based on this, a suboptimal fading factor (SFF) is derived and embedded in the predicted covariance to weaken the influence of prior information on the filtering solution only in the presence of kinematic model error. In addition to correction of the UKF estimation in the presence of kinematic model error, the RSTUKF also maintains the optimal UKF estimation in the absence of kinematic model error. Simulation and experimental analysis demonstrate the performance of the proposed approach to INS/GNSS integration.
Subject Automation and Control Engineering
Keyword(s) Direct filtering
INS/GNSS integration
Kinematic model error
Strong tracking unscented Kalman filter
Suboptimal fading factor
DOI - identifier 10.1016/j.ast.2018.03.040
Copyright notice © 2018 Elsevier Masson SAS. All rights reserved.
ISSN 1270-9638
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Citation counts: TR Web of Science Citation Count  Cited 23 times in Thomson Reuters Web of Science Article | Citations
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