An outlier-robust Kalman filter

Proceedings - IEEE International Conference on Robotics and Automation

Volumn , Issue , 2011, Pages 1551-1558

  Agamennoni, Gabriel ^a   Nieto, Juan I ^a   Nebot, Eduardo M ^a  

^a UNIVERSITY OF SYDNEY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

DISCRETE-TIME MODEL; HEAVY-TAILED; NON-GAUSSIAN; RECURSIONS; SEQUENTIAL DATA; SMOOTHING ALGORITHMS; STANDARD KALMAN FILTERS; SYNTHETIC AND REAL DATA;

ALGORITHMS; STATISTICS; SYNTHETIC APERTURE SONAR;

ROBOTICS;

EID: 79960798108     PISSN: 10504729     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICRA.2011.5979605     Document Type: Conference Paper

References (32)

1. S. Roweis and Z. Ghahramani, "A unifying review of linear-Gaussian models," Neural Computation, vol. 11, no. 2, pp. 305-345, 1999.
2. R. Shumway and D. Stoffer, Time Series Analysis and its Applications. Springer-Verlag New York, 2005.
3. J. Morris, "The Kalman filter: A robust estimator for some classes of linear-quadratic problems," IEEE Transactions on Information Theory, vol. 22, pp. 526-534, 1976.
4. P. Huber, "Robust estimation of a location parameter," Annals of Mathematical Statistics, vol. 35, no. 1, pp. 73-101, 1964.
5. V. Barnett and T. Lewis, Outliers in Statistical Data. John Wiley & Sons, 1994.
6. H. Sorenson and D. Alspach, "Recursive Bayesian estimation using Gaussian sums," Automatica, vol. 7, no. 4, pp. 465-479, 1971.
7. M. West, "Robust sequential approximate Bayesian estimation," Journal of the Royal Statistical Society, Series B (Methodological), vol. 43, no. 2, pp. 157-166, 1981.
8. R. Meinhold and N. Singpurwalla, "Robustification of Kalman filter models," Journal of the American Statistical Association, pp. 479-486, 1989.
9. W. Wu and A. Kundu, "Recursive filtering with non-Gaussian noises," IEEE Transactions on Signal Processing, vol. 44, no. 6, pp. 1454-1468, June 1996.
10. C. Masreliez and R. Martin, "Robust Bayesian estimation for the linear model and robustifying the Kalman filter," IEEE Transactions on Automatic Control, vol. 22, no. 3, pp. 361-371, June 1977.
11. I. Schick and S. Mitter, "Robust recursive estimation in the presence of heavy-tailed observation noise," The Annals of Statistics, vol. 22, no. 2, pp. 1045-1080, June 1994.
12. Z. Durovic and B. Kovacevic, "Robust estimation with unknown noise statistics," IEEE Transactions on Automatic Control, vol. 44, no. 6, pp. 1292-1296, 1999.
13. G. Kitagawa, "Non-Gaussian state-space modeling of nonstationary time series," Journal of the American Statistical Association, vol. 82, no. 400, pp. 1032-1041, 1987.
14. S. Kramer and H. Sorenson, "Recursive Bayesian estimation using piece-wise constant approximations," Automatica, vol. 24, no. 6, pp. 789-801, 1988.
15. G. Kitagawa, "Monte Carlo filter and smoother for non-Gaussian nonlinear state-space models," Journal of the American Statistical Association, vol. 93, pp. 1203-1215, 1996.
16. N. Gordon, "Approximate non-Gaussian Bayesian estimation and modal consistency," Journal of the Royal Statistical Society: Series B (Methodological), vol. 55, no. 4, pp. 913-918, 1993.
17. -, "A hybrid bootstrap filter for target tracking in clutter," IEEE Transactions on Aerospace and Electronic Systems, vol. 33, no. 1, pp. 353-358, January 1997.
18. M. Isard and A. Blake, "Condensation - conditional density propagation for visual tracking," International Journal of Computer Vision, vol. 29, no. 1, pp. 5-28, August 1998.
19. R. van der Merwe, N. de Freitas, A. Doucet, and E. Wan, "The un-scented particle filter," in Advances in Neural Information Processing Systems, November 2001.
20. J. Ting, E. Theodorou, and S. Schaal, "A Kalman filter for robust outlier detection," in Proceedings of the IEEE International Conference on Intelligent Robots and Systems, 2007.
21. -, "Learning an outlier-robust Kalman filter," in Proceedings of the 2007 European Conference on Machine Learning, 2007.
22. S. Sarkka and A. Nummenmaa, "Recursive noise adaptive Kalman filtering by variational Bayesian approximations," IEEE Transactions on Automatic Control, vol. 54, no. 3, pp. 596-600, March 2009.
23. K. Murphy, "Dynamic Bayesian networks: Representation, inference and learning," Ph.D. dissertation, University of California, Berkeley, 2002.
24. A. Dawid, "Some matrix-variate distribution theory: Notational considerations and a Bayesian application," Biometrika, vol. 68, pp. 265-274, 1981.
25. W. Gosset, "The probable error of a mean," Biometrika, vol. 6, no. 1, pp. 1-25, 1908.
26. M. Beal, "Variational algorithms for approximate Bayesian inference," Ph.D. dissertation, Gatsby Computational Neuroscience Unit, University College London, 2003.
27. S. Kullback and R. Leibler, "On information and sufficiency," Annals of Mathematical Statistics, vol. 22, no. 1, pp. 79-86, 1951.
28. R. Bucy and P. Joseph, Filtering for Stochastic Processes with Applications to Guidance. American Mathematical Society Chelsea, 2005.
29. Z. Ghahramani and G. Hinton, "Variational learning for switching state-space models," Neural Computation, vol. 12, no. 4, pp. 831-864, 2000.
30. A. Dempster, N. Laird, and D. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," Journal of the Royal Statistical Society, Series B, vol. 39, no. 1, pp. 1-38, 1977.
31. Z. Ghahramani and G. Hinton, "Parameter estimation for linear dynamical systems," University of Toronto, Tech. Rep. CRG-TR-96-2, February 1996.
32. M. Abramowitz and I. Stegun, Handbook of Mathematical Functions, with Formulas, Graphs and Mathematical Tables. Dover Publications, Incorporated, 1974.