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Low-cost integrated INS/GNSS using adaptive H∞ Cubature Kalman Filter

Published online by Cambridge University Press:  07 February 2023

S. Taghizadeh  and
R. Safabakhsh
S. Taghizadeh
Affiliation:
Department of Computer Engineering, Amirkabir University of Technology, Tehran 15875-4413, Iran
R. Safabakhsh*
Affiliation:
Department of Computer Engineering, Amirkabir University of Technology, Tehran 15875-4413, Iran
*
*Corresponding author. E-mail: safa@aut.ac.ir
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Abstract

We proposed an adaptive H-infinity Cubature Kalman Filter (AH∞CKF) to improve the navigation accuracy of a highly manoeuvrable unmanned aerial vehicle (UAV). AH∞CKF fuses the Inertial Navigation System (INS) and Global Navigation Satellite System (GNSS) measurements. Traditional state estimation filters like extended Kalman filters (EKF) and cubature Kalman filters (CKF) assume Gaussian noises. However, their performance degrades for non-Gaussian noises and system uncertainties encountered in real-world applications. Thus, designing filters robust to noise and distribution is crucial. AH∞CKF combines H∞CKF design with an added adaptive factor to adjust the state estimation covariance matrix according to measurements by exploiting the square root method to yield more numerically stable results (SrAH∞CKF). We conducted multiple dynamically rich flight tests to validate our claims using a UAV equipped with a commercially well-known GNSS solution. Results show that the SrAH∞CKF state estimation outperforms EKF and CKF methods on average by 90% in various statistical measures.

Keywords

error state Kalman filterintegrated navigation systemrobust estimationunmanned aerial system
Type
Research Article
Information
The Journal of Navigation , Volume 76 , Issue 1 , January 2023 , pp. 1 - 19
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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