Gaussian mixture Sigma-Point particle filters for sequential probabilistic inference in dynamic state-space models

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn 6, Issue , 2003, Pages 701-704

  Van der Merwe, Rudolph ^a   Wan, Eric ^a  

^a OREGON HEALTH AND SCIENCE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; ESTIMATION; STATE SPACE METHODS;

PARTICLE FILTERS;

KALMAN FILTERING;

EID: 0141630418     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

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3. S. Julier, J. Uhlmann, and H. Durrant-Whyte, "A new approach for filtering nonlinear systems," in Proceedings of the American Control Conference, 1995, pp. 1628-1632.
4. M. Norgaard, N. Poulsen, and O. Ravn, "New Developments in State Estimation for Nonlinear Systems," Automatica, vol. 36, 2000.
5. E. A. Wan and R. van der Merwe, Kalman Filtering and Neural Networks, chapter 7 - The Unscented Kalman Filter, Wiley, 2001.
6. R. van der Merwe and E. Wan, "Efficient Derivative-Free Kalman Filters for Online Learning," in Proc. of ESANN, Bruges, Apr. 2001.
7. A. Doucet, N. de Freitas, and N. Gordon, Sequential Monte-Carlo Methods in Practice, Springer-Verlag, April 2001.
8. R. van der Merwe, Probabilistic Inference using Sigma-Point Kalman Filters, Ph.D. thesis, OGI School of Science & Engineering, Oregon Health & Science University, January 2003, In preparation.
9. R. van der Merwe, N. de Freitas, A. Doucet, and E. Wan, "The Unscented Particle Filter," in Advances in Neural Information Processing Systems 13, Nov 2001.
10. G.J. McLachlan and T. Krishnan, The EM Algorithm and Extensions, Wiley, 1997.