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A switched adaptive strategy for state estimation in MEMS-based inertial navigation systems with application for a flight test

Published online by Cambridge University Press:  02 April 2024

Mohammad Saber Fadaki ,
Hamid Reza Koofigar Open the ORCID record for Hamid Reza Koofigar [Opens in a new window] ,
Mohsen Ekramian  and
Mahdi Mortazavi
Mohammad Saber Fadaki
Affiliation:
Department of Electrical Engineering, University of Isfahan, Isfahan 81746-73441, Iran
Hamid Reza Koofigar*
Affiliation:
Department of Electrical Engineering, University of Isfahan, Isfahan 81746-73441, Iran
Mohsen Ekramian
Affiliation:
Department of Electrical Engineering, University of Isfahan, Isfahan 81746-73441, Iran
Mahdi Mortazavi
Affiliation:
Department of Mechanical Engineering, University of Isfahan, Isfahan 81746-73441, Iran
*
*Corresponding author: Hamid Reza Koofigar; Email: koofigar@eng.ui.ac.ir
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Abstract

This paper proposes a switched model to improve the estimation of Euler angles and decrease the inertial navigation system (INS) error, when the centrifugal acceleration occurs. Depending on the situation, one of the subsystems of the proposed switched model is activated for the estimation procedure. During global positioning system (GPS) outages, an extended Kalman filter (EKF) operates in the prediction mode and corrects the INS information, based on the system error model. Compared with previous works, the main advantages of the proposed switched-based adaptive EKF (SAEKF) method are (i) elimination of INS error, during the centrifugal acceleration, and (ii) high accuracy in estimating the attitude and positioning, particularly during GPS outages. To validate the efficiency of the proposed method in various trajectories, an experimental flight test is performed and discussed, involving a microelectromechanical (MEMS)-based INS. The comparative study shows that the proposed method considerably improves the accuracy in various scenarios.

Keywords

flightGPS outagesMEMS-based inertial navigation systemswitched-based adaptive EKF
Type
Research Article
Information
The Journal of Navigation , First View , pp. 1 - 22
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Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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