Model based learning of sigma points in unscented Kalman filtering

Neurocomputing

Volumn 80, Issue , 2012, Pages 47-53

  Turner, Ryan ^a   Rasmussen, Carl Edward ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

Gaussian process; Global optimization; Machine learning; Sigma points; State space; Unscented Kalman filtering

Indexed keywords

GAUSSIAN PROCESSES; MACHINE-LEARNING; SIGMA POINT; STATE-SPACE; UNSCENTED KALMAN FILTERING;

COMPUTATIONAL COMPLEXITY; GLOBAL OPTIMIZATION; TIME SERIES;

KALMAN FILTERS;

ARTICLE; CONTROLLED STUDY; INFORMATION PROCESSING; INTERMETHOD COMPARISON; KERNEL METHOD; LINEAR SYSTEM; MACHINE LEARNING; MATHEMATICAL ANALYSIS; MODEL BASED LEARNING; NONLINEAR SYSTEM; PREDICTION; PRIORITY JOURNAL; PROBABILITY; PROCESS OPTIMIZATION;

EID: 84855224603     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.07.029     Document Type: Article

References (18)

1. Kalman R.E. A new approach to linear filtering and prediction problems. Trans. ASME-J. Basic Eng. 1960, 82:35-45.
2. Maybeck P.S. Stochastic Models, Estimation, and Control. Mathematics in Science and Engineering 1979, vol. 141. Academic Press, Inc.
3. S.J. Julier, J.K. Uhlmann, A new extension of the Kalman filter to nonlinear systems, in: Proceedings of AeroSense: 11th Symposium on Aerospace/Defense Sensing, Simulation and Controls, Orlando, FL, USA, 1997, pp. 182-193.
4. Arasaratnam I., Haykin S. Cubature Kalman filters. IEEE Trans. Autom. Control 2009, 54(6):1254-1269.
5. Julier S.J., Uhlmann J.K. Unscented filtering and nonlinear estimation. Proc. IEEE 2004, 92(3):401-422. 10.1109/JPROC.2003.823141.
6. Wan E.A., van der Merwe R. The unscented Kalman filter for nonlinear estimation. Symposium 2000 on Adaptive Systems for Signal Processing, Communication and Control 2000, 153-158. IEEE, Lake Louise, AB, Canada.
7. Thrun S., Burgard W., Fox D. Probabilistic Robotics 2005, The MIT Press, Cambridge, MA, USA.
8. Julier S.J., Uhlrnann J.K., Durrant-Whyte H.F. A new approach for filtering nonlinear systems. American Control Conference 1995, vol. 3:1628-1632. IEEE, Seattle, WA, USA. 10.1109/ACC.1995.529783.
9. Snelson E., Ghahramani Z. Compact approximations to Bayesian predictive distributions. Proceedings of the 22nd International Conference on Machine Learning 2005, 840-847. Omnipress, Bonn, Germany.
10. M.A. Osborne, R. Garnett, S.J. Roberts, Gaussian processes for global optimization, in: 3rd International Conference on Learning and Intelligent Optimization (LION3), Trento, Italy, 2009.
11. Srinivas N., Krause A., Kakade S., Seeger M. Gaussian process optimization in the bandit setting: no regret and experimental design. Proceedings of the 27th International Conference on Machine Learning 2010, 1015-1022. Omnipress, Haifa, Israel. J. Fürnkranz, T. Joachims (Eds.).
12. Rasmussen C.E., Ghahramani Z. Bayesian Monte Carlo. Advances in Neural Information Processing Systems, vol. 15 2003, 489-496. The MIT Press, Cambridge, MA, USA. S. Becker, S. Thrun, K. Obermayer (Eds.).
13. Sutton R.S., Barto A.G. Reinforcement Learning: An Introduction 1998, The MIT Press, Cambridge, MA, USA.
14. R. Turner, M.P. Deisenroth, C.E. Rasmussen, State-space inference and learning with Gaussian processes, in: the 13th International Conference on Artificial Intelligence and Statistics, vol. 9, Sardinia, Italy, 2010, pp. 868-875.
15. Deisenroth M.P., Huber M.F., Hanebeck U.D. Analytic moment-based Gaussian process filtering. Proceedings of the 26th International Conference on Machine Learning 2009, 225-232. Omnipress, Montreal, QC, Canada.
16. Kitagawa G. Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. J. Computat. Graphical Stat. 1996, 5(1):1-25.
17. M.P. Deisenroth, C.E. Rasmussen, J. Peters, Model-based reinforcement learning with continuous states and actions, in: Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN 2008), Bruges, Belgium, 2008, pp. 19-24.
18. Bishop C.M. Pattern Recognition and Machine Learning 2007, Springer. first ed.