Risk-sensitive particle filters for mitigating sample impoverishment

IEEE Transactions on Signal Processing

Volumn 56, Issue 10 II, 2008, Pages 5001-5012

  Orguner, Umut ^a   Gustafsson, Fredrik ^a  

^a LINKÖPING UNIVERSITY   (Sweden)

Author keywords

Depletion; Particle filter; Risk sensitive; Sample degeneracy; Sample impoverishment

Indexed keywords

EID: 54749115304     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSP.2008.928520     Document Type: Article

References (23)

1. W. Gilks, S. Richardson, and D. Spiegelhalter, Markov Chain Monte Carlo in Practice. London, U.K.: Chapman and Hall, 1996.
2. Sequential Monte Carlo Methods in Practice, A. Doucet, N. de Freitas, and N. Gordon, Eds. New York: Springer-Verlag, 2001.
3. A. Doucet, S. Godsill, and C. Andrieu, "On sequential simulation-based methods for Bayesian filtering," Statist. Comput., vol. 10, no. 3, pp.197-208, 2000.
4. N. Gordon, D. Salmond, and A. Smith, "A novel approach to nonlinear/ non-Gaussian Bayesian state estimation," Inst. Elect. Eng. Proc. Radar Signal Process., vol. 140, no. 2, pp. 107-113, Apr. 1993.
5. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, "A tutorial on particle filters for on-line nonlinear/non-Gaussian Bayesian tracking," IEEE Trans. Signal Process., vol. 50, pp. 174-188, Feb. 2002.
6. B. Ristic, S. Arulampalam, and N. Gordon, Beyond the Kalman Filter: Particle Filters For Tracking Applications. London, U.K.: Artech House, 2004.
7. P. Fearnhead, "Sequential Monte Carlo methods in filter theory," Ph.D. dissertation, Univ. Oxford, Oxford, U.K., 1998.
8. A. Doucet, N. Gordon, and V. Krishnamurthy, "Particle filters for state estimation of jump Markov linear systems," IEEE Trans. Signal Process., vol. 49, pp. 613-624, Mar. 2001.
9. M. Pitt and N. Shephard, "Filtering via simulation: Auxiliary particle filters," J. Amer. Statist. Assoc., vol. 94, no. 446, pp. 590-599, Jun. 1999.
10. R. K. Boel, M. R. James, and 1. R. Petersen, "Robustness and risk-sensitive filtering," IEEE Trans. Autom. Control, vol. 47, pp. 451-461, Mar. 2002.
11. S. Thrun, J. Langford, and V. Verma, "Risk sensitive particle filters," in Advances in Neural Information Processing Systems 14 Cambridge: MIT Press, 2002.
12. S. Sadhu and A. Doucet, "Particle methods for risk sensitive filtering," in Proc. IEEE Indicon 2005, Chennai, India, 2005, pp. 423-426.
13. S. Bhaumik, T. Ghoshal, and S. Sadhu, "Alternative formulation of risk-sensitive particle filter (posterior)," in Proc. IEEE Indicon 2006, 2006, pp.1-4.
14. U. Orguner and F. Gustafsson, "Risk sensitive particle filters for mitigating sample impoverishment," in Proc. IEEE Workshop Statist. Signal Process., Aug. 2007, pp. 259-263.
15. R. J. Elliott, L. Aggoun, and J. B. Moore, Hidden Markov Models: Estimation and Control. New York: Springer-Verlag, 1994.
16. S. Dey and J. B. Moore, "Risk-sensitive filtering and smoothing via reference probability methods," IEEE Trans. Autom. Control, vol. 42, pp. 1587-1591, Nov. 1997.
17. V. R. Ramezam, "Product estimators for hidden Markov Models," Ph.D. dissertation, Dep. Elect. Eng., Univ. Maryland, College Park, 2001.
18. J. L. Speyer, C. Fan, and R. N. Banavar, "Optimal stochastic estimation with exponential cost criteria," in Proc. 31st Conf. Decision Control, 1992, vol. 2, pp. 2293-2298.
19. S. Dey and J. B. Moore, in Proc. Amer. Control Conf., Seattle, WA, 1995, vol. 1, pp. 129-133.
20. L. Aggoun and R. Elliott, Measure Theory and Filtering: Introduction with Applications. Cambridge, U.K.: Cambridge Univ. Press, 2004.
21. P. R. Kumar and P. Varaiya, Stochastic Systems: Estimation, Identification and Adaptive Control. Englewood Cliffs, NJ: Prentice-Hall, 1986.
22. J. B. Moore, R. J. Elliott, and S. Dey, "Risk-sensitive generalizations of minimum variance estimation and control," J. Math. Syst., Estimat. Control, vol. 7, no. 1, pp. 1-15, 1997.
23. S. Dey and J. B. Moore, "Finite-dimensional risk-sensitive filters and smoothers for discrete-time nonlinear systems," IEEE Trans. Autom. Control, vol. 44, pp. 1234-1239, Jun. 1999.