An introduction to Gaussian processes for the Kalman filter expert

13th Conference on Information Fusion, Fusion 2010

Volumn , Issue , 2010, Pages

  Reece, Steven ^a   Roberts, Stephen ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

Bayesian methods; Gaussian processes; Kalman filter; Kriged Kalman filter; Near constant acceleration model

Indexed keywords

BAYESIAN NETWORKS; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC);

BAYESIAN METHODS; CONSTANT ACCELERATION; GAUSSIAN PROCESSES; HYBRID FILTERS; KRIGED KALMAN FILTERS; MATHEMATICAL CONCEPTS; SPATIAL TEMPORALS; STATE BASED APPROACHES;

KALMAN FILTERS;

EID: 79952429674     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/icif.2010.5711863     Document Type: Conference Paper

References (15)

1. Y. Bar-Shalom and T. E. Fortmann. Tracking and Data Association. Academic Press, 1988.
2. C. M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
3. B. Ferris, D. Haehnel, and D. Fox. Gaussian processes for signal strength-based location estimation. Robotics: Science and Systems, 2006.
4. C. Guestrin, A. Krause, and A. Singh. Near-optimal sensor placements in gaussian processes. In 22nd International Conference on Machine Learning (ICML), pages 265-272. ICML, 2005. (Pubitemid 43183342)
5. A. H. Jaswinski. Stochastic Processes and Filtering Theory. New York: Academic Press, 1970.
6. S. J. Julier and J. K. Uhlmann. A new extension of the kalman filter to nonlinear systems. In In Int. Symp. Aerospace/Defense Sensing, Simul. and Controls, pages 182-193, 1997.
7. S. J. Julier and J. K. Uhlmann. A non-divergent estimation algorithm in the presence of unknown correlations. In Proceedings of the 1997 American Control Conference (ACC'97), Albuquerque, New Mexico, pages 2369-2373, 1997.
8. J. Ko, D. J. Klein, D. Fox, and D. Haehnel. Gp-ukf: Unscented kalman filters with gaussian process prediction and observation models. In IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007, pages 1901-1907, 2007.
9. D. J. Leith, M. Heidl, and J. V. Ringwood. Gaussian process prior models for electrical load forecasting. In Eighth International Conference on Probabilistic Methods Applied to Power Systems, Iowa State University, Ames, Sept. 12-16, 2004.
10. D. J. C. Mackay. Introduction to gaussian processes. In C. M. Bishop, editor, Neural Networks and Machine Learning. Springer-Verlag, 1998.
11. K. Mardia, C. Goodall, E. Redfern, and F. Alonso. The kriged kalman filter (with discussion). TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 7(2):217-282, December 1998.
12. M. Osborne, A. Rogers, A. Ramchurn, S. Roberts, and N. R. Jennings. Towards real-time information processing of sensor network data using computationally efficient multi-output gaussian processes. In IPSN 2008: International Conference on Information Processing in Sensor Networks, St. Louis, Missouri, 2008.
13. C. E. Rasmussen and C. K. I.Williams. Gaussian Processes for Machine Learning. The MIT Press, 2006.
14. S. Reece and S. Roberts. The near constant acceleration model gaussian process kernel. IEEE Transactions on Signal Processing Letters, 2010. In press.
15. S. Särkkä, A Vehtari, and J Lampinen. Prediction of estsp competition time series by unscented kalman filter and rts smoother. In Proceedings of the European Symposium on Time Series Prediction (ESTSP), Espoo, Finland, 2007.