Recursive outlier-robust filtering and smoothing for nonlinear systems using the multivariate student-t distribution

IEEE International Workshop on Machine Learning for Signal Processing, MLSP

Volumn , Issue , 2012, Pages

  Piché, Robert ^a   Särkkä, Simo ^b   Hartikainen, Jouni ^b  

^a TAMPERE UNIVERSITY OF TECHNOLOGY   (Finland)

^b AALTO UNIVERSITY   (Finland)

Author keywords

Gaussian filter; Gaussian smoother; Robust filtering; Robust smoothing; Variational Bayes

Indexed keywords

GAUSSIAN FILTERS; GAUSSIANS; ROBUST FILTERING; ROBUST SMOOTHING; VARIATIONAL BAYES;

COMPUTER SIMULATION; LEARNING SYSTEMS; STATE SPACE METHODS;

SIGNAL PROCESSING;

EID: 84870703036     PISSN: 21610363     EISSN: 21610371     Source Type: Conference Proceeding    
DOI: 10.1109/MLSP.2012.6349794     Document Type: Conference Paper

References (18)

1. K. L. Lange, R. J. A. Little, and J. M. G. Taylor, "Robust statistical modeling using the t distribution," Journal of the American Statistical Association, vol. 84, no. 408, pp. 881-896, 1989
2. R. J. A. Little and D. B. Rubin, Statistical Analysis with Missing Data, Wiley Series in Probability and Statistics. Wiley, New York, 1987
3. A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin, Bayesian Data Analysis, Chapman & Hall/CRC, second edition, 2004
4. R. Pich́e, "Robust multivariate linear regression using the Student-t distribution," Tech. Rep. , Tampere University of Technology, Tampere, Finland, May 2012
5. G. Agamennoni, J. I. Nieto, and E. M. Nebot, "An outlier-robust Kalman filter," in IEEE Int. Conf. on Robotics and Automation (ICRA), May 2011, pp. 1551-1558
6. S. Särkkä and A. Nummenmaa, "Recursive noise adaptive Kalman filtering by variational Bayesian approximations," IEEE Transactions on Automatic Control, vol. 54, no. 3, pp. 596-600, 2009
7. J.-A. Ting, E. Theodorou, and S. Schaal, "A Kalman filter for robust outlier detection," in Proc. IEEE/RSJ Intl. Conf. Intelligent Robots and Systems, Oct. 2007, pp. 1514-1519
8. P. Maybeck, Stochastic Models, Estimation and Control, vol. 2, Academic Press, 1982
9. K. Ito and K. Xiong, "Gaussian filters for nonlinear filtering problems," IEEE Transactions on Automatic Control, vol. 45, no. 5, pp. 910-927, May 2000
10. Y. Wu, D. Hu, M. Wu, and X. Hu, "A numericalintegration perspective on Gaussian filters," IEEE Transactions on Signal Processing, vol. 54, no. 8, pp. 2910-2921, August 2006
11. S. Särkkä and J. Hartikainen, "On Gaussian optimal smoothing of non-linear state space models," IEEE Transactions on Automatic Control, vol. 55, no. 8, pp. 1938-1941, August 2010
12. A. H. Jazwinski, Stochastic Processes and Filtering Theory, Academic Press, 1970
13. S. J. Julier, J. K. Uhlmann, and H. F. Durrant-Whyte, "A new approach for filtering nonlinear systems," in Proceedings of the 1995 American Control, Conference, Seattle, Washington, 1995, pp. 1628-1632
14. I. Arasaratnam and S. Haykin, "Cubature Kalman filters," IEEE Transactions on Automatic Control, vol. 54, no. 6, pp. 1254-1269, June 2009
15. S. Särkkä, "Unscented Rauch-Tung-Striebel smoother," IEEE Transactions on Automatic Control, vol. 53, no. 3, pp. 845-849, April 2008 (Pubitemid 351597197)
16. I. Arasaratnam and S. Haykin, "Cubature Kalman smoothers," Automatica, vol. 47, no. 10, pp. 2245-2250, 2011
17. C. M. Bishop, Pattern Recognition and Machine Learning, Springer-Verlag New York, Inc. , Secaucus, NJ, USA, 2006
18. S. Särkkä, A. Vehtari, and J. Lampinen, "Rao-Blackwellized particle filter for multiple target tracking," Information Fusion, vol. 8, no. 1, pp. 2-15, 2007. (Pubitemid 44751677)