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In-motion Alignment for Low-cost SINS/GPS under Random Misalignment Angles

Published online by Cambridge University Press:  22 June 2017

Xiao Cui ,
Chunbo Mei ,
Yongyuan Qin ,
Gongmin Yan  and
Qiangwen Fu
Xiao Cui*
Affiliation:
(College of Automation, Northwestern Polytechnical University, China)
Chunbo Mei
Affiliation:
(No.203 Research Institute of China Ordnance Industries, China)
Yongyuan Qin
Affiliation:
(College of Automation, Northwestern Polytechnical University, China)
Gongmin Yan
Affiliation:
(College of Automation, Northwestern Polytechnical University, China)
Qiangwen Fu
Affiliation:
(College of Automation, Northwestern Polytechnical University, China)
*
(E-mail: [email protected])
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Abstract

This paper presents a reliable in-motion alignment algorithm for a low cost Strapdown Inertial Navigation System/Global Positioning System (SINS/GPS) combination under random misalignment angles, which transforms attitude alignment into an attitude estimation problem. Based on Rodrigues parameters, an alignment model with a linear state-space equation and a second order nonlinear measurement equation is established. Furthermore, by employing a Taylor expansion on the nonlinear measurement equation, we implement a second order Extended Kalman Filter (EKF2). The proposed method uses a single filter that can not only determine the initial attitude, but also estimate the sensor errors. In addition, a scheme is given for avoiding singularity, which makes the algorithm more widely suitable for random misalignment angles. Experimental ground tests are performed with a low-cost Micro-Electromechanical System (MEMS) SINS, which validates the efficacy of the proposed method. The performance compared to the traditional alignment algorithm is also given.

Keywords

In-motion alignmentInertial navigation systemRodrigues parametersSecond order Extended Kalman Filter (EKF2)
Type
Research Article
Information
The Journal of Navigation , Volume 70 , Issue 6 , November 2017 , pp. 1224 - 1240
Copyright
Copyright © The Royal Institute of Navigation 2017 

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