Diagnosis by a waiter and a Mars explorer

Proceedings of the IEEE

Volumn 92, Issue 3, 2004, Pages 455-468

  De Freitas, Nando ^a   Dearden, Richard ^b   Hutter, Frank ^c   Morales Menéndez, Rubén ^a,d   Mutch, Jim ^a   Poole, David ^a  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^b NASA AMES RESEARCH CENTER   (United States)

^c DARMSTADT UNIVERSITY OF TECHNOLOGY   (Germany)

^d SCHOOL OF ENGINEERING AND SCIENCES   (Mexico)

Author keywords

Diagnosis; Rao Blackwellized particle filtering; Robotics; State estimation

Indexed keywords

ALGORITHMS; DIAGNOSIS; ERROR ANALYSIS; KALMAN FILTERING; MATHEMATICAL MODELS; ROBOTICS; STATE ESTIMATION; WIRELESS TELECOMMUNICATION SYSTEMS;

DISCRETE STATES; DYNAMIC BAYESIAN NETWORKS (DBN); MARGINALIZATION; RAO-BLACKWELLIZED PARTICLE FILTERING;

MARTIAN SURFACE ANALYSIS;

EID: 10444220031     PISSN: 00189219     EISSN: None     Source Type: Journal    
DOI: 10.1109/JPROC.2003.823157     Document Type: Conference Paper

References (29)

1. S. Thrun, J. Langford, and V. Verma, "Risk sensitive particle filters," in Advances in Neural Information Processing Systems 14, S. B. T. G. Dietterich and Z. Ghahramani, Eds. Cambridge, MA: MIT Press, 2002.
2. R. Morales-Menendez, N. de Freitas, and D. Poole, "Real-time monitoring of complex industrial processes with particle filters," in Advances in Neural Information Processing Systems 16. Cambridge, MA: MIT Press, 2003.
3. R. Dearden and D. Clancy, "Particle filters for real-time fault detection in planetary rovers," in Proc. Int. Workshop Diagnosis, 2001, pp. 1-6.
4. V. Verma, J. Fernandez, and R. Simmons, "Probabilistic models for monitoring and fault diagnosis," presented at the 2nd IARP and IEEE/RAS Joint Workshop on Technical Challenges for Dependable Robots in Human Environment. Toulouse, France, 2002.
5. P. Elinas, J. Hoey, D. Lahey, J. D. Montgomery, D. Murray, S. Se, and J. J. Little, "Waiting with José, a vision-based mobile robot," in Proc: Int. Conf. Robotics and Automation, 2002, pp. 3698-3705.
6. N. Metropolis and S. Ulam, "The Monte Carlo method," J. Amer. Stat. Assoc., vol. 44, no. 247, pp. 335-341, 1949.
7. A. Doucet, N. de Freitas, and N. J. Gordon, Eds., Sequential Monte Carlo Methods in Practice. New York: Springer-Verlag, 2001.
8. A. Doucet, N. de Freitas, K. Murphy, and S. Russell, "Rao- Blackwellised particle filtering for dynamic Bayesian networks," in Uncertainty in Artificial Intelligence, C. Boutilier and M. Godszmidt, Eds. San Mateo, CA: Morgan Kaufmann, 2000, pp. 176-183.
9. G. Casella and C. P. Robert, "Rao-Blackwellization of sampling schemes," Biometrika, vol. 83, no. 1, pp. 81-94, 1996.
10. H. Akashi and H. Kumamoto, "Random sampling approach to state estimation in switching environments," Automatica, vol. 13, pp. 429-434, 1977.
11. A. Doucet, "On sequential simulation-based methods for Bayesian filtering," Dept. Eng., Cambridge Univ., Cambridge, U.K., Tech. Rep. CUED/F-INFENG/TR 310, 1998.
12. A. Doucet, S. Godsill, and C. Andrieu, "On sequential Monte Carlo sampling methods for Bayesian filtering," Stat. Comput., vol. 10, no. 3, pp. 197-208, 2000.
13. A. Kong, J. S. Liu, and W. H. Wong, "Sequential imputations and Bayesian missing data problems," J. Amer. Stat. Assoc., vol. 89, no. 425, pp. 278-288, 1994.
14. S. N. MacEachern, M. Clyde, and J. S. Liu, "Sequential importance sampling for nonparametric Bayes models: The next generation," Can. J. Stat., vol. 27, pp. 251-267, 1999.
15. A. Doucet, N. J. Gordon, and V. Krishnamurthy, "Particle filters for state estimation of jump Markov linear systems," Eng. Dept., Cambridge Univ., Cambridge, U.K., Tech. Rep. CUED/F-INFENG/TR 359, 1999.
16. S.Roweis and Z. Ghahramani, "A unifying review of linear Gaussian models," Neural Comput., vol. 11, no. 2, pp. 305-345, 1999.
17. R. H. Shumway and D. S. Stoffer, "An approach to time series smoothing and forecasting using the EM algorithm," J. Time Series Anal., vol. 3, no. 4, pp. 253-264, 1982.
18. N. de Freitas, M. Niranjan, A. H. Gee, and A. Doucet, "Global optimization of neural network models via sequential sampling," in Advances in Neural Information Processing Systems, M. J. Kearns, S. A. Solla, and D. Cohn, Eds. Cambridge, MA; MIT Press, 1999, vol. 11, pp. 410-416.
19. J. Geweke, "Bayesian inference in econometric models using Monte Carlo integration," Econometrica, vol. 24, pp. 1317-1399, 1989.
20. G. Kitagawa, "Monte Carlo filter and smoother for non-Gaussian nonlinear state space models," J. Comput. Graph. Stat., vol. 5, pp. 1-25, 1996.
21. N. de Freitas, "Rao-Blackwellised particle filtering for fault diagnosis," in Proc. IEEE Aerospace Conf., vol. 4, 2001, pp. 4-1767-4-1772.
22. C. Andrieu, A. Doucet, and E. Punskaya, "Sequential Monte Carlo methods for optimal filtering," in Sequential Monte Carlo Methods in Practice, A. Doucet, N. de Freitas, and N. J. Gordon, Eds. New York: Springer-Verlag, 2001.
23. M. K. Pitt and N. Shephard, "Filtering via simulation: Auxiliary particle filters," J. Amer. Stat. Assoc., vol. 94, no. 446, pp. 590-599, 1999.
24. L. Ljung, System Identification: Theory for the User. Englewood Cliffs, NJ: Prentice-Hall, 1987.
25. R. Washington, "On-board real-time state and fault identification for rovers," in Proc. IEEE Int. Conf. Robotics and Automation, vol. 2, 2000, pp. 1175-1181.
26. R. Morales-Menendez, N. de Freitas, and D. Poole, "Estimation and control of industrial processes with particle filters," in Proc. American Control Conf., 2003, pp. 579-584.
27. C. Andrieu, M. Davy, and A. Doucet, "Efficient particle filtering for jump Markov systems," IEEE Trans. Signal Processing, vol. 51, pp. 1762-1770, July 2003.
28. F. Hutter and R. Dearden, "Efficient on-line fault diagnosis for non-linear systems," presented at the 7th Int. Symp. Artificial Intelligence, Robotics and Automation in Space, Nara, Japan, 2003.
29. _, "The Gaussian particle filter for diagnosis of nonlinear systems," in Proc. 14th Int. Conf. Principles of Diagnosis, 2003, pp. 65-70.