Unscented Kalman filter with controlled adaptation

IFAC Proceedings Volumes (IFAC-PapersOnline)

Volumn 16, Issue PART 1, 2012, Pages 906-911

  Straka, Ondřej ^a   Duník, Jindřich ^a   Simandl, Miroslav ^a  

^a UNIVERSITY OF WEST BOHEMIA   (Czech Republic)

Author keywords

Estimation theory; Kalman filtering; Nonlinear stochastic system; State estimation

Indexed keywords

COMPUTATION THEORY; NONLINEAR ANALYSIS; STATE ESTIMATION; STOCHASTIC CONTROL SYSTEMS; STOCHASTIC SYSTEMS;

COMPUTATIONAL COSTS; ESTIMATION THEORY; KALMAN-FILTERING; MEASUREMENT FUNCTION; NON-LINEAR STOCHASTIC SYSTEMS; SCALING PARAMETER; STRONGLY NONLINEAR; UNSCENTED KALMAN FILTER;

KALMAN FILTERS;

EID: 84867092168     PISSN: 14746670     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3182/20120711-3-BE-2027.00163     Document Type: Conference Paper

References (14)

1. B. D. O. Anderson and J. B. Moore. Optimal Filtering. Englewood Cliffs, New Jersey: Prentice Hall Ins., 1979.
2. I. Arasaratnam and S. Haykin. Cubature Kalman filters. IEEE Transactions on Automatic Control, 54(6):1254-1269, 2009.
3. Y. Bar-Shalom, X. R. Li, and T Kirubarajan. Estimation with Applications to Tracking and Navigation: Theory Algorithms and Software. John Wiley & Sons, 2001.
4. J. Dunik, M. Simandl, and O. Straka. Adaptive choice of scaling parameter in derivative-free local filters. In Proceedings of the 13th International Conference on Information Fusion, Edinburgh, Great Britain, 2010.
5. K. Ito and K. Xiong. Gaussian filters for nonlinear filtering problems. IEEE Transactions on Automatic Control, 45(5): 910-927, 2000.
6. B. Jia, M. Xin, and Y. Cheng. Sparse Gauss-Hermite quadrature filter with application to spacecraft attitude estimation. Journal of Guidance, Control, and Dynamics, 34(2), 2011.
7. S. J. Julier and J. K. Uhlmann. Unscented filtering and nonlinear estimation. IEEE Review, 92(3):401-421, 2004.
8. S. J. Julier, J. K. Uhlmann, and H. F. Durrant-Whyte. A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Transactions on Automatic Control, 45(3):477-482, 2000.
9. M. Norgaard, N. K. Poulsen, and O. Ravn. New developments in state estimation for nonlinear systems. Automatica, 36 (11):1627-1638, 2000.
10. B. Ristic, S. Arulampalam, and N. Gordon. Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House, 2004.
11. M. Simandl and J. Dunik. Derivative-free estimation methods: New results and performance analysis. Automatica, 45(7): 1749-1757., 2009.
12. M. Stojmenovic, A. Nayak, and J. Zunic. Measuring linearity of a finite set of points. In Cybernetics and Intelligent Systems, 2006 IEEE Conference on, pages 1-6, 2006.
13. O. Straka, J. Dunik, and M. Simandl. Performance evaluation of local state estimation methods in bearings-only tracking problems. In Proceedings of the 14th International Conference on Information Fusion, Chicago, IL., 2011a.
14. O. Straka, J. Dunik, and M. Simandl. Gaussian sum unscented Kalman filter with adaptive scaling parameters. In Proceedings of the 14th International Conference on Information Fusion, Chicago, IL., 2011b.