Exact Bayesian and particle filtering of stochastic hybrid systems

IEEE Transactions on Aerospace and Electronic Systems

Volumn 43, Issue 1, 2007, Pages 55-70

  Blom, Henk A P ^a   Bloem, Edwin A ^a  

^a NATIONAL AEROSPACE LABORATORY NLR   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; COMPUTER SIMULATION; MONTE CARLO METHODS; PROBABILITY;

DISCRETE MODE VALUE; EUCLIDEAN VALUE; INTERACTING MULTIPLE MODEL; PARTICLE FILTER;

STOCHASTIC PROGRAMMING;

EID: 34248674968     PISSN: 00189251     EISSN: None     Source Type: Journal    
DOI: 10.1109/TAES.2007.357154     Document Type: Article

References (48)

1. Bar-Shalom, Y., Challa, S., and Blom, H. A. P. IMM estimator versus optimal estimator for hybrid systems. IEEE Transactions on Aerospace and Electronic Systems, 41 (2005), 986-991.
2. Mathews, V. J., and Tugnait, J. K. Detection and estimation with fixed lag for abruptly changing systems. IEEE Transactions on Aerospace and Electronic Systems, 19 (1983), 730-739.
3. Campo, L., Mookerjee, P., and Bar-Shalom, Y. State estimation for systems with a sojourn-time-dependent Markov switching model. IEEE Transactions on Automatic Control, 36 (1991), 238-243.
4. Blom, H. A. P. Hybrid state estimation for systems with semi-Markov switching coefficients. Proceedings of the 1st European Control Conference, Grenoble, 1991, 1132-1137.
5. Koutsoukis, X., Kurien, J., and Zhao, F. Monitoring and diagnosis of hybrid systems using particle filtering methods. Proceedings of Mathematical Theory of Networks and Systems (MTNS), 2002.
6. McGinnity, S., and Irwin, G. W. Multiple model bootstrap filter for maneuvering target tracking. IEEE Transactions on Aerospace and Electronic Systems, 36 (2000), 1006-1012.
7. McGinnity, S., and Irwin, G. W. Maneuvering target tracking using a multiple-model bootstrap filter. In A. Doucet, N. de Freitas, and N. Gordon (Eds.) Sequential Monte Carlo Methods in Practice, New York: Springer, 2001, 479-497.
8. Angelova, D. S., Semerdjiev, T. A., Jilkov, V. P., and Semerdjiev, E. A. Application of Monte Carlo method for tracking maneuvering target in clutter. Mathematics and Computers in Simulation, 1851 (2000), 1-9.
9. Chen, R., and Liu, J. S. Mixture Kalman filters. Journal of Royal Statistical Society B, 62, Part 3 (2000), 493-508.
10. Doucet, A., Logothetis, A., and Krishnamurthy, V. Stochastic sampling algorithms for state estimation of jump Markov linear systems. IEEE Transactions on Automatic Control, 45 (2000), 188-202.
11. Doucet, A., Gordon, N. J., and Krishnamurthy, V. Particle filters for state estimation of jump Markov linear systems. IEEE Transactions on Signal Processing, 49 (2001), 613-624.
12. Musso, C., Oudjane, N., and Le Gland, F. Improving regularised particle filters. In A. Doucet, N. de Freitas, and N. Gordon (Eds.), Sequential Monte Carlo Methods in Practice, New York: Springer, 2001, 247-271.
13. Andrieu, C., Davy, M., and Doucet, A. Efficient particle filtering for jump Markov systems. Application to time-varying autoregressions. IEEE Transactions on Signal Processing, 51 (2003), 1762-1770.
14. Blom, H. A. P., and Bloem, E. A. Joint IMMPDA particle filter. Proceedings of 6th International Conference on Information Fusion, 2003, 785-792.
15. Boers, Y., and Driessen, H. Hybrid state estimation: A target tracking application. Automatica, 38 (2002), 2153-2158.
16. Boers, Y., and Driessen, J. N. Interacting multiple model particle filter. IEE Proceedings of Radar, Sonar and Navigation, 150 (2003), 344-349.
17. Driessen, H., and Boers, Y. An efficient particle filter for jump Markov nonlinear systems. Proceedings of IEE Colloquium on Target Tracking Mar. 2004.
18. Ristic, B., Arulampalam, S., and Gordon, N. Beyond the Kalman filter - Particle filters for tracking applications. Norwood, MA: Artech House, 2004.
19. Morelande, M., and Challa, S. Maneuvering target tracking in clutter using particle filters. IEEE Transactions on Aerospace and Electronic Systems, 41 (2005), 252-270.
20. Rubin, D. B. Using the SIR algorithm to simulate posterior distributions. In J. M. Bernardo et al. (Eds.), Bayesian Statistics 3 New York: Oxford University Press, 1988, 395-402.
21. Gordon, N. J., Salmond, D. J., and Smith, A. F. M. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings-F 140 (1993), 107-113.
22. Del Moral, P. Nonlinear filtering: Interacting particle solution. Markov Processes and Related Fields, 2 (1996), 555-580.
23. Kitagawa, G. Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. Journal of Computational and Graphical Statistics 5 (1996), 1-25.
24. Doucet, A. On sequential simulation-based methods for Bayesian filtering. Technical report CUED/F-INFENG/TR-310, University of Cambridge, UK, 1998
25. Del Moral, P., and Guionnet, A. Large deviation for interacting particle systems - Application to nonlinear filtering problems. Stochastic Processes and their Applications, 78 (1998), 69-95.
26. Crisan, D., and Doucet, A. A survey of convergence results on particle filtering methods for practitioners. IEEE Transactions on Signal Processing, 50 (2002), 736-746.
27. Daum, F. E., and Huang, J. The curse of dimensionality for particle filters. Proceedings of IEEE Conference on Aerospace, Big Sky, MT, Mar. 2003.
28. Isard, M., and Blake, A. Condensation - Conditional density propagation for visual tracking. Journal of Computer Vision, 29 (1998), 5-28.
29. Boers, Y., Driessen, J. N., Verschure, F., and Heemels, W. P. M. H., and Juloski, A. A multi target track before detect application. Proceedings of IEEE CVPR Conference (workshop on multi object tracking) Madison, WI, June 2003.
30. Liu, J. S., and Chen, R. Sequential Monte Carlo methods for dynamical systems. Journal of American Statistical Association, 93 (1998), 1032-1044.
31. Gilks, W. R., and Berzuini, C. Following a moving target - Monte Carlo inference for dynamic Bayesian models. Journal of Royal Statistical Society B, 63 (2001), 127-146.
32. Doucet, A., Godsill, S., and Andrieu, C. On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 10 (2000), 197-208.
33. Pitt, M. K., and Shephard, N. Filtering via simulation: Auxiliary particle filters. Journal of American Statistical Association, 94 (1999), 590-599.
34. Hürzeler, M., and Künsch, H. R. Monte Carlo approximations for general state-space models. Journal of Computational and Graphical Statistics, 7 (1998), 175-193.
35. Arulampam, M. S., Maskell, S., Gordon, N., and Clapp, T. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 50 (2002), 174-188.
36. Rui, Y., and Chen, Y. Better proposal distributions: Object tracking using unscented particle filter. Proceedings IEEE CVPR Conference, Kauai, Hawaii, Dec. 2001, II-786-793.
37. Casella, G., and Robert, C. Rao-Blackwellisation of sampling schemes. Biometrika, 83 (1996), 81-94.
38. Gustafsson, F., Gunnarsson, F., Bergman, N., Forssell, U., Jansson, J., Karlsson, R., Nordlund, P.-J. Particle filters for positioning, navigation and tracking. IEEE Transactions on Signal Processing, 50 (2002), 425-437.
39. Maskell, S., Rollason, M., Gordon, N., and Salmond, D. Efficient particle filtering for multiple target tracking with application to tracking in structured images. Proceedings of SPIE, Signal and Data Processing of Small Targets, vol. 4728, Apr. 2002.
40. Morelande, M. R., and Challa, S. An algorithm for tracking group targets. Proceedings of Workshop on Multiple Hypothesis Tracking: A tribute to S. Blackman, San Diego, CA, May 2003.
41. Blom, H. A. P., and Bloem, E. A. Particle filtering for stochastic hybrid systems. Proceedings of IEEE Conference on Decision and Control, Bahamas, Dec. 2004.
42. Blom, H. A. P. An efficient filter for abruptly changing systems. Proceedings of the 23rd IEEE Conference on Decision and Control, 1984, 656-658.
43. Bernardo, J. M., and Smith, A. F. M. Bayesian Theory. New York: Wiley, 1994.
44. Tugnait, J. K., and Haddad, A. H. A detection-estimation scheme for state estimation in switching environments. Automatica, 15 (1979), 477-481.
45. Elliott, R. J., Dufour, F., and Sworder, D. D. Exact hybrid filters in discrete time. IEEE Transactions on Automatic Control, 41 (1996), 1807-1810.
46. Blom, H. A. P., and Bar-Shalom, Y. The interacting multiple model algorithm for systems with Markovian switching coefficients. IEEE Transactions on Automatic Control, 33 (1988), 780-783.
47. Bar-Shalom, Y., and Li, X. R. Estimation and Tracking: Principles, Techniques and Software. Norwood, MA: Artech House, 1993.
48. Bar-Shalom, Y., Li, X. R., and Kirubarajan, T. Estimation, Tracking and Navigation. New York: Wiley-Interscience, 2001.