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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: [email protected])
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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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References

REFERENCES

Burton, A.M. (2000). Improving the Accuracy and Resolution of SINS/DGPS Airborne Gravimetry. Ph.D. dissertation, Dept. Geomat.Eng., Calgary Univ., Calgary, AB, Canada.Google Scholar
Chen, L., Wang, G.H., Jia, S.Y. and Progri, I. (2012). Attitude Bias Conversion Model for Mobile Radar Error Registration. Journal of Navigation, 65(4), 651–670.CrossRefGoogle Scholar
Chen, S.Y. and Ma, H.C. (2012). Boresight calibration of airborne LIDAR system without ground control points. IEEE Transactions on Geoscience & Remote SensingLetters, 9(1), 85–89.Google Scholar
De Macedo, K.A.C., Scheiber, R. and Moreira, A. (2007). An Autofocus Approach for Residual motion errors with applications to airborne repeat-pass SAR interferometry. IEEE Transactions on Geoscience & Remote Sensing, 46(10), 3151–3162.Google Scholar
DZ/T 0082-2006. (2006).The Standard for Regional Gravity Survey. People's Republic of China Geological and Mineral Industry Standards.Google Scholar
Fang, J. and Yang, S. (2011). Study on Innovation Adaptive EKF for In-Flight Alignment of Airborne POS. IEEE Transactions on Instrumentation & Measurement, 60(4), 1378–1388.Google Scholar
Fang, J., Chen, L. and Yao, J. (2014). An Accurate Gravity Compensation Method for High-Precision Airborne POS. IEEE Transactions on Geoscience & Remote Sensing, 52(8), 4564–4573.Google Scholar
Grejner-Brzezinska, D.A. and Wang, J. (1998). Gravity Modelling for High-Accuracy GPS/INS Integration. Navigation, 45(3), 209–220.Google Scholar
He, Z.K., Liu, G.B., Zhao, X.J. and Yang, J. (2013). Temperature Model for FOG Zero-Bias Using Gaussian Process Regression. Intelligence Computation and Evolutionary Computation. 180, 37–45.Google Scholar
Hirt, C., Claessens, S., Fecher, T., Kuhn, M., Pail, R. and Rexer, M. (2013). New ultra-high resolution picture of Earth's gravity field. Geophysical Research Letters, 40(16), 4279–4283.Google Scholar
NovAtel. (2016). OEM638™ Triple-Frequency GNSS Receiver. https://www.novatel.com/products/gnss-receivers/oem-receiver-boards/oem6-receivers/oem638/.Google Scholar
Kwon, J.H. (2004). Gravity Compensation Methods for Precision INS. Proceedings of Annual Meeting of the Institute of Navigation.Google Scholar
Lawrence, N. (2005). Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models. Journal of Machine Learning Research, 6(3), 1783–1816.Google Scholar
Li, X.Q., Shao, M.X., Paik, H.J., Huang, Y.C., Song, T.X. and Bian, X. (2014). Effects of satellite positioning errors and Earth's multipole moments in the detection of the gravitomagnetic field with an orbiting gravity gradiometer. General Relativity & Gravitation, 46(46), 1–14.CrossRefGoogle Scholar
Mostafa, M.M. R. and Hutton, J. (2001). Direct position and orientation system: How do they work? What is the attainable accuracy? Proceedings of the American Society of Photogrammetry and Remote Sensing Annual Meeting, St. Louis, MO, USA,1-11.Google Scholar
Nagai, M., Chen, T.E. and Shibasaki, R. (2009). UAV-borne 3-D mapping systemby multisensor integration. IEEE Transactions on Geoscience & Remote Sensing, 47(3), 701–708.Google Scholar
Nassar, S. (2003). Improving the Inertial Navigation System(INS) Error Model for INS and INS/DGPS Applications. Ph.D. dissertation, Department of Geomatics Engineering, the University of Calgary. Calgary, Alberta, Canada.Google Scholar
Park, C.W., Huang, J.H.Z. and Ding, Y. (2011). Domain decomposition approach for fast Gaussian process regression of large spatial data sets. Journal of Machine Learning Research, V12, 1697–1728.Google Scholar
Petelin, D. and Kocijan, J. (2011). Control system with evolving Gaussian process model. Proceeding of IEEE Symposium Series on Computational Intelligence, Paris, 178–184.Google Scholar
Siouris, G. M. (2009). Gravity modeling in aerospace applications. Aerospace Science & Technology, 13(6), 301–315.CrossRefGoogle Scholar
Wang, H., Xiao, X., Deng, Z.H. and Fu, M.Y. (2014). The Influence of Gravity Disturbance on High-Precision Long-Time INS and Its Compensation Method. International Conference on Instrumentation & Measurement, IEEE, 104–108.Google Scholar
Wang, Q.Y., Wang, Z.Y., Fu, Z.J., Liu, W.Y. and Lin, Q. (2016). A compact laser system for the cold atom gravimeter. Optics Communications, 358, 82–87.Google Scholar
Zhao, L., Liu, F. and Yuan, H. (2009). The design and analysis for accelerometer-based gravity gradiometer. IEEE International Conference on Mechatronics and Automation (ICMA), 2409–2414.Google Scholar