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A Dual-IMU/GPS based Geolocation System

Published online by Cambridge University Press:  25 November 2011

Jong Ki Lee  and
Christopher Jekeli
Jong Ki Lee
Affiliation:
(Division of Geodetic Science, School of Earth Science, The Ohio State University, Columbus, OH 43210)
Christopher Jekeli*
Affiliation:
(Division of Geodetic Science, School of Earth Science, The Ohio State University, Columbus, OH 43210)
*
(E-mail: jkl2608@gmail.com)
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Abstract

To improve the geolocation performance of an Unexploded Ordnance (UXO) survey platform, a geodetic Global Positioning System (GPS) receiver was combined with two tactical-grade Inertial Measurement Units (IMUs) and mounted on two types of detection systems. Analysis of data collected for typical trajectories focused on the dual-IMU/GPS pre/post processing using optimal nonlinear estimation together with a Wave Correlation Filter (WCF) and end-matching. Each trajectory of the platforms was estimated by the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). The WCF was then applied to the two solutions of the platform trajectories derived from each IMU in order to extract the common components in the frequency domain, assuming that uncorrelated components are errors. The remaining bias and trends of the estimated position results were further removed by end-matching IMU solutions and GPS update points. The results of these methods were applied to our field test data to show that the WCF and end-matching can improve position accuracy from 4% to 14% with respect to the Unscented Kalman Smoother (UKS) solution alone.

Keywords

Wave Correlation FilterEnd-matchingDual-IMU/GPSUnexploded Ordnance
Type
Research Article
Information
The Journal of Navigation , Volume 65 , Issue 1 , January 2012 , pp. 113 - 123
Copyright
Copyright © The Royal Institute of Navigation 2011

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References

REFERENCES

Bell, T. (2005). Geo-location Requirements for UXO Discrimination. Presented at SERDP & ESTCP Geolocation Workshop, Annapolis, MD.Google Scholar
Daubechies, I. (1992). Ten Lectures on Wavelets, CBMS-NSF Regional Conference Series in Applied Mathematics.CrossRefGoogle Scholar
Jekeli, C. (2000). Inertial Navigation Systems with Geodetic Applications, Walter deGruyter, Inc., Berlin.Google Scholar
Julier, S. J., Uhlmann, J. K. and Durrant-Whyte, H. F. (1995). A new approach for filtering nonlinear systems. Proceedings of the American Control Conference, Seattle, WA.CrossRefGoogle Scholar
Julier, S. J. and Uhlmann, J. K. (1996). A general method for approximating nonlinear transformations of probability distributions. Technical report, Department of Engineering Science, University of Oxford, Oxford, England.Google Scholar
Julier, S. J., Uhlmann, J. K. and Durrant-Whyte, H. F. (2000). A new approach for nonlinear transformations of means and covariances in filters and estimators. IEEE Transactions on Automatic Control, 45(3), 477–482.CrossRefGoogle Scholar
Lee, J. K. (2009). The Estimation Methods for an Integrated INS/GPS UXO Geolocation System. OSU report 493.Google Scholar
Li, X. (2007). Moving Base INS/GPS Vector Gravimetry on a Land Vehicle. OSU report 486.Google Scholar
Nassar, S. (2003). Improving the Inertial Navigation System (INS) Error Model for INS and INS/DGPS Applications. Ph.D. Thesis, University of Calgary, UCGE Report No.20183.Google Scholar
Rogers, R. M. (2000). Applied Mathematics in Integrated Navigation Systems. AIAA Education Series, American Institute of Aeronautics and Astronautics, Inc., Reston, VA.Google Scholar
Sarkka, S. (2008), Unscented Rauch–Tung–Striebel Smoother. Automatic Control, IEEE Transactions on, 53–3, 845–849.Google Scholar
Schaffrin, B. (1995). On some alternatives to Kalman filtering. Geodetic Theory Today, 235–245, ed. Sans`o, F., Springer Series, IAG-Symp., No. 114, Springer, BerlinCrossRefGoogle Scholar
Serpas, J. G. (2003). Local and Regional Geoid Determination from Vector Airborne Gravimetry. OSU report 468.Google Scholar
Simms, J. and Carin, L. (2004). Innovative navigation systems to support digital geophysical mapping ESTCP #200129 phase II demonstrations. Revised Report, U.S. Army Corps of Engineers Engineer Research and Development Center, Vicksburg, MS.Google Scholar
Titterton, D. H. and Weston, J. L. (2004). Strapdown Inertial Navigation Technology. Peter Peregrinus, Piscataway, NJ, 2004.CrossRefGoogle Scholar
Van der Merwe, R., Doucet, A., de Freitas, N. and Wan, E. (2000). The unscented particle filter. Technical Report CUED/F-INFENG/TR 380, Engineering Department,Cambridge University, Cambridge, England.Google Scholar
Wan, E. A. and van Der Merwe, R. (2001). The unscented Kalman filter. Chapter 7 in: Simon Haykin (Ed.), Kalman Filtering and Neural Networks, John Wiley & Sons, New York, 2001.Google Scholar