Sampling densities of particle filter: A survey and comparison

Proceedings of the American Control Conference

Volumn , Issue , 2007, Pages 4437-4442

  Šimandl, Miroslav ^a   Straka, Ondřej ^a  

^a UNIVERSITY OF WEST BOHEMIA   (Czech Republic)

Author keywords

Nonlinear filtering; Particle filter; Sampling density; State estimation

Indexed keywords

AIR FILTERS; ESTIMATION; NONLINEAR FILTERING; PARAMETER ESTIMATION; SIGNAL FILTERING AND PREDICTION; SURVEYS; WAVE FILTERS;

DISCRETE TIME (DT); ESTIMATION QUALITY; IMPORTANCE FUNCTION (IF); KEY PARAMETERS; NON-GAUSSIAN; PARTICLE FILTER (PF); SAMPLING DENSITIES;

SAMPLING;

EID: 46449135650     PISSN: 07431619     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ACC.2007.4282447     Document Type: Conference Paper

References (35)

1. N. Gordon, D. Salmond, and A. Smith, "Novel approach to nonlinear/non-Gaussian Bayesian state estimation," IEE proceedings-F, vol. 140, pp. 107-113, 1993.
2. J. Handshin, "Monte Carlo techniques for prediction and filtering of nonlinear stochastic processes," Automatica, vol. 6, pp. 553-563, 1970.
3. J. Handshin and D. Mayne, "Monte Carlo techniques to estimate the condidional expectation in multi-stage non-linear filtering," International Journal of Control, vol. 9, pp. 547-559, 1969.
4. V. Zaritskii, V. Svetnik, and L. Shimelevich, "Monte Carlo technique in problems of optimal data processing," Automatic Remote Control, pp. 95-103, 1975.
5. A. Doucet, N. de Freitas, and N. Gordon, Eds., Sequential Monte Carlo Methods in Practice. Springer, 2001, (Ed. Doucet A., de Freitas N., and Gordon N.).
6. D. Fox, "KLD-sampling: Adaptive particle filters and mobile robot localization," in Advances in Neural Information Processing Systems (NIPS), 2001.
7. O. Straka and M. Šimandl, "Adaptive particle filter based on fixed efficient sample size," in Preprints of the 14th IFAC Symposium on System Identification, Newcastle, 2006.
8. J. Liu and R. Chen, "Sequential Monte Carlo Methods for Dynamic Systems," J. Amer. Statist. Assoc., vol. 93, no. 443, pp. 1032-1044, 1998.
9. J. Liu, R. Cheng, and T. Logvinenko, Sequential Monte Carlo Methods in Practice, ser. Statistics for Engineering and Information Science. Springer, 2001, ch. A Theoretical Framework for Sequential Importance Sampling with Resampling, pp. 225-246, (Ed. Doucet A., de Freitas N., and Gordon N.).
10. J. Geweke, "Bayesian inference in econometric models using Monte Carlo Integration," Econometrika, vol. 24, pp. 1317-1399, 1989.
11. Y Huang and P. Djuric, "A hybrid importance function for particle filtering," IEEE Signal Processing Letters, vol. 11, no. 3, pp. 404-406, 2004.
12. M. Pitt and N. Shephard, Sequential Monte Carlo Methods in Practice. Springer, 2001, ch. Auxiliary Variable Based Particle Filter, pp. 273-294, (Ed. Doucet A., de Freitas N., and Gordon N.).
13. R. van der Merwe and E. Wan, "Sigma-point particle filters for sequential probabilistic inference in dynamic state-space models," in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP). Hong Kong: IEEE, 4 2003.
14. H. Akashi and H. Kumamoto, "Random sampling approach to state estimation in switching environments," Automatica, pp. 429-434, 1977.
15. H. Tanizaki, Nonlinear Filters: Estimation and Applications, ser. Lecture Notes in Economics and Mathematical Systems. Berlin: Springer, 1993.
16. M. Pitt and N. Shephard, "Filtering via simulation: auxiliary particle filter," J. Amer. Statist. Assoc., vol. 94, pp. 590-599, 1999.
17. C. Andrieu, M. Davy, and A. Doucet, "Improved auxiliary particle filtering: Applications to time-varying spectral analysis," IEEE Workshop on Statistical Signal Processing, 2001.
18. S. Julier and J. Uhlmann, "The scaled unscented transformation, " in Proceedings of the IEEE American Control Conference. Anchorage AK, USA: IEEE, 2002, pp. 4555-4559.
19. O. Straka and M. Šimandl, "Using the bhattacharyya distance in functional sampling density of particle filter," in Preprints of the 16th IFAC World Congress, P. Horacek, M. Simandl, and P. Zitek, Eds. Prague: IFAC, 2005.
20. Z. Chen, "Bayesian filtering: From kalman filters to particle filters, and beyond." 2003, [Online], http://soma.crl.mcmaster.ca/zhechen/ homepage.htm.
21. _, "Robust particle filters with application in tracking and communications," Adaptive Syst. Lab., McMaster Univ., Hamilton, ON, Canada, Tech. Rep., 2003.
22. J. De Freitas, M. Niranjan, A. Gee, and A. Doucet, "Sequential Monte Carlo Methods to Train Neural Network Models," Neural Computation, no. 12, pp. 955-993, 2000.
23. S. Godsill, A. Doucet, and M. West, "Maximum a posteriori sequence estimation using Monte Carlo particle filters," Ann. Inst. Statist. Math, no. 1, pp. 82-96, 2001.
24. S. Godsill and T. Clapp, Sequential Monte Carlo Methods in Practice, ser. Statistics for Engineering and Information Science. Springer, 2001, ch. Improvement Strategies for monte Carlo Particle Filters, pp. 139-158, (Ed. Doucet A., de Freitas N., and Gordon N.).
25. R. Neal, "Annealed importance sampling," University of Toronto, Toronto, Ontario, Canada, Tech. Rep. 9805, 1998.
26. J. MacCormick and A. Blake, "A probabilistic exclusion principle for tracking multiple objects," in Proc. 7th International Conf. Computer Vision, 1999, pp. 572-578.
27. J. MacCormick and M. Isard, "Partitioned sampling, articulated objects, and interface-quality hand tracking," Tech. Rep., 2000.
28. A. Doucet, S. Godsill, and C. Andrieu, "On sequential Monte Carlo sampling methods for Bayesian filtering," Statistics and Computing, vol. 10, no. 3, pp. 197-208, 2000.
29. R. Chen and J. Liu, "Mixture Kalman filters," Journal of the Royal Statistics Society, vol. 62, pp. 493-508, 2000.
30. H. Sorenson and D. Alspach, "Approximation of Density Function by a Sum of Gaussians for Nonlinear Bayesian Estimation," in Proceedings of the 1st Symposium for Nonlinear Estimation Theory and Its Applications, San Diego, 1970, pp. 88-90.
31. _, "Recursive Bayesian Estimation Using Gaussian Sums," Automatica, vol. 7, pp. 465-479, 1971.
32. J. Kotecha and P. Djuric, "Gaussian sum particle filtering," in IEEE Transactions on Signal Processing, ser. 51, no. 10, 2003.
33. S. Julier, J. Uhlmann, and H. Durrant-Whyte, "A new approach for filtering nonlinear systems," in Proceedings of the American Control Conference, 1995, pp. 1628-1632.
34. K. Nishiyama, "Fast and effective generation of the proposal distribution for particle filters," Signal Processing, no. 85, pp. 2412-2417, 2005.
35. M. Šimandl, J. Královec, and T. Söderström, "Anticipative Grid Design in Point-Mass Approach to Nonlinear State Estimation," IEEE Transactions on Automatic Control, vol. 47, no. 4, 2002.