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http://dx.doi.org/10.11108/kagis.2010.13.2.157

The Unscented Kalman Filter Based Backward Filters for the Precise INS/GPS System  

Kwon, Jay-Hyoun (Dept. of Geoinformatics, The University of Seoul)
Lee, Jong-Ki (Civil & Environmental Engineering & Geodetic Science, The Ohio State University)
Lee, Ji-Sun (Dept. of Geoinformatics, The University of Seoul)
Publication Information
Journal of the Korean Association of Geographic Information Studies / v.13, no.2, 2010 , pp. 157-167 More about this Journal
Abstract
Unscented Kalman filter based backward filter is derived and the positions from extended Kalman filter, unscented Kalman filter, and extended Kalman smoother are compared and analyzed through a simulation test. Considering the poor GPS signal reception, the simulation is performed under the assumption of only the start and end points of the trajectory, composed of 4 curves and 5 straight sections in the area of $40m{\times}40m $, are known. The test shows that the smoothers generate much better positioning results of 8~9m improvement compared to those from the forward filters. For the comparison between the smoothers, the analysis is performed separately for the curves and straight segments. In both cases, the unscented Kalman smoother generates better positioning error; 10cm and 23cm improved positioning results in straight segment and curves, respectively.
Keywords
Extended Kalman Filter; Unscented Kalman Filter; Extended Kalman Smoother; Unscented Kalman Smoother;
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  • Reference
1 Anderson, B.D.O. and J.B. Moore. 1979. Optimal Filtering, Prentice Hall, New York, NY. 368pp.
2 Haykin, S. 2001. Kalman Filtering and Neural Networks. John Wiley & Sons, Inc., New York. 284pp.
3 Julier, S.J. and J.K. Uhlmann. 1997. A general method for approximating nonlinear transformations of probability distributions, Technical report, Department of Engineering Science, University of Oxford, Oxford, England. 27pp.
4 Julier, S.J., J.K. Uhlmann and H.F. Durrant-Whyte. 1995. A new approach for filtering nonlinear systems. Proc. of the American Control Conference, Seattle, WA, pp.1625-1632.
5 Julier, S.J., J.K. Uhlmann and H.F. Durrant-Whyte. 2000. A new approach for nonlinear transformations of means and covariances in filters and estimators. IEEE Transactions on Automatic Control 45(3):477-482.   DOI   ScienceOn
6 Kalman, R.E. 1960. A New Approach to Linear Filtering and Prediction Problems. Trans. of the ASME-Journal of Basic Engineering 82(D):35-45.   DOI
7 Klass, M., M. Briers, N. de Freitas, A. Doucet, S. Maskell and D. Lang. 2006. Fast particle smoothing: If I had a million particles, in Proc. ICML 2006, Pittsburgh, PA, pp.481-488.
8 Lee, J.K. and C. Jekeli. 2009. Improved Filter Strategies for Precise Unexploded Ordnance Geolocation using IMU/GPS integration, Journal of Navigation 62(3):365-382.   DOI   ScienceOn
9 Maybeck, P. S. 1979. Stochastic models, estimation and control: Academic Press, New York. 423pp.
10 Rauch, H. E., F. Tung and C. T. Striebel. 1965. Maximum likelihood estimates of linear dynamic systems, AIAA J 3(2): 1445-1450.   DOI
11 Sarkka, S. 2008. Unscented Rauch -Tung -Striebel Smoother, Automatic Control, IEEE Transactions on, 53(3): 845-849.
12 Shin, E.H. 2005. Estimation Techniques for Low-Cost Inertial Navigation. Ph.D. Thesis, University of Calgary. UCGE Report 20219. 181pp.
13 Van der Merwe, R., A. Doucet, N. de Freitas and E. Wan. 2000. The unscented particle filter. Technical Report CUED/F-INFENG/TR 380, Engineering Department, Cambridge University, Cambridge, England.
14 Wan, E.A. and R. van Der Merwe. 2001. The unscented Kalman filter. Chapter 7 in: Simon Haykin (Ed.), Kalman Filtering and Neural Networks, John Wiley & Sons, New York. 50pp.
15 Welch, G. and G. Bishop. 2001. An Introduction to the Kalman Filter. Chapel Hill. SIGGRAPH. 16pp.