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Velocity-aided In-motion Alignment for SINS Based on Pseudo-Earth Frame

Published online by Cambridge University Press:  10 August 2017

Meng Liu ,
Guangchun Li ,
Yanbin Gao ,
Shutong Li  and
Lianwu Guan
Meng Liu
Affiliation:
(College of Automation, Harbin Engineering University, Harbin 150001, China)
Guangchun Li*
Affiliation:
(College of Automation, Harbin Engineering University, Harbin 150001, China)
Yanbin Gao
Affiliation:
(College of Automation, Harbin Engineering University, Harbin 150001, China)
Shutong Li
Affiliation:
(College of Automation, Harbin Engineering University, Harbin 150001, China)
Lianwu Guan
Affiliation:
(College of Automation, Harbin Engineering University, Harbin 150001, China)
*
(E-mail: [email protected])
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Abstract

Approaching the problem from the internal factors and in particular the inherent state model of a Kalman Filter, this paper presents a novel Strapdown Inertial Navigation System (SINS) modelling, which is obtained with a pseudo-north-oriented mechanisation in a pseudo-geographic frame. Improved modelling associated with the backward algorithm is proposed to achieve velocity-aided in-motion alignment. Compared with traditional algorithms, the proposed method can eliminate the influence of alignment model on the performance of initial alignment caused by SINS modelling. On the other hand, the backward process can still be used to accelerate the process of alignment. As a result, the proposed method is expected to assist those methods only considered from external factors (such as coarse accuracy, process noise, measurement noise, and so on) to improve the stability and robustness of a velocity-aided in-motion alignment system and to solve the modelling problem of high latitude alignment without sacrificing alignment accuracy. Finally, simulations and field experiments with a navigation-grade SINS demonstrate the superior performance of the proposed method.

Keywords

In-motion alignmentPseudo-Earth frameHigh latitude alignmentKalman Filter (KF)
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 1 , January 2018 , pp. 221 - 240
Copyright
Copyright © The Royal Institute of Navigation 2017 

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