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A Real-time Gravity Compensation Method for a High-Precision Airborne Position and Orientation System based on a Gravity Map

Published online by Cambridge University Press:  28 November 2017

Zhuangsheng Zhu ,
Yiyang Guo  and
Wen Ye
Zhuangsheng Zhu
Affiliation:
(Key laboratory of Fundamental Science for National Defense, Beijing, 100191, China) (Novel Inertial Instrument & Navigation System Technology, Beijing, 100191, China) (Beihang University, Beijing, 100191, China)
Yiyang Guo*
Affiliation:
(Key laboratory of Fundamental Science for National Defense, Beijing, 100191, China) (Novel Inertial Instrument & Navigation System Technology, Beijing, 100191, China) (Beihang University, Beijing, 100191, China)
Wen Ye
Affiliation:
(Key laboratory of Fundamental Science for National Defense, Beijing, 100191, China) (Novel Inertial Instrument & Navigation System Technology, Beijing, 100191, China) (Beihang University, Beijing, 100191, China)
*
(E-mail: me_gyy@163.com)
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Abstract

Motion compensation is a significant part of an airborne remote sensing system. A Position and Orientation System (POS) can directly measure the motion information of an airborne remote sensing payload that can improve the quality of airborne remote sensing images. Gravity disturbance, information on which is often ignored due to being difficult to acquire in real-time, has become the main error source of POS in the development of inertial components. In this paper, a new real-time gravity compensation method is proposed, which includes the gravity disturbance as the error states of a POS Kalman filter, and an accurate gravity disturbance model is constructed using a time-varying Gaussian-Markov model based on a high-precision gravity map, whose resolution is enhanced by a new interpolation method based on Gaussian Process Regression (GPR). A flight experiment was conducted to evaluate the efficiency of the proposed method and the results showed that the proposed method performs well when compared with other real-time gravity compensation methods.

Keywords

Gravity compensationGravity disturbanceGaussian Process RegressionGravity mapGaussian-Markov Model
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 3 , May 2018 , pp. 711 - 728
Copyright
Copyright © The Royal Institute of Navigation 2017 

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