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Tytuł artykułu

Auxiliary and Rao-Blackwellised particle filters comparison

Autorzy
Treść / Zawartość
Identyfikatory
Warianty tytułu
Konferencja
Computer Applications in Electrical Engineering 2013 (15-16.04.2013; Poznań, Polska)
Języki publikacji
EN
Abstrakty
EN
Particle filters are very popular - number of algorithms based on Sequential Monte Carlo methods is growing. Paper describes and compares the performance of two of them - Auxiliary and Rao-Blackwellised Particle Filters. Comparison includes also Bootstrap Filter and some variety of SIR algorithm.
Rocznik
Tom
Strony
79--88
Opis fizyczny
Bibliogr. 18 poz., rys., tab.
Twórcy
autor
  • Poznań University of Technology
autor
  • Poznań University of Technology
Bibliografia
  • [1] Arulampalam S., Maskell S., Gordon N., Clapp T., A Tutorial on Particle Filters for On-line Non-linear/Non-Gaussian Bayesian Tracking, IEEE Proceedings on Signal Processing, Vol.50, No.2, 2002, pp. 174-188.
  • [2] Brzozowska-Rup K., Dawidowicz A.L., Metoda filtru cząsteczkowego, Matematyka Stosowana: matematyka dla społeczeństwa 2009, T. 10/51, pp. 69-107.
  • [3] Candy J.V., Bayesian signal processing, WILEY, New Jersey 2009, pp. 19-44, 237-298.
  • [4] Chang C., Ansari R., Khokhar A., Multiple Object Tracking with Kernel Particle Filter, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 2005, Vol. 1, pp.566-573.
  • [5] Douc R., Cappe O., Moulines E., Comparison of Resampling Schemes for Particle Filtering, Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, September 2005, pp. 64-69.
  • [6] Doucet A., Freitas N., Murphy K., Russell S., Rao-Blackwellised particle filtering for dynamic Bayesian networks, Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence, pp. 176-183.
  • [7] Doucet A., Godsill S., Andrieu C., On sequential Monte Carlo sampling methods for Bayesian filtering, Statistics and Computing, 10, 2000, pp. 197-208.
  • [8] Doucet A., Johansen A.M., A Tutorial on Particle Filtering and Smoothing: Fifteen years later, handbook of Nonlinear Filtering 2009/12, pp. 656-704.
  • [9] Gordon N.J., Salmond N.J., Smith A.F.M., Novel approach to nonlinear/non-Gaussian Bayesian state estimation, IEE Proceedings-F, Vol.140, No.2, 1993, pp. 107-113.
  • [10] Handeby G., Karlsson R., Gustafsson F., The Rao-Blackwellized Particle Filter: A Filter Bank Implementation, EURASIP Journal on Advances in Signal Processing, Volume 2010, Article ID 724087, pp. 10.
  • [11] Kozierski P., Lis M., Filtr cząsteczkowy w problemie śledzenia - wprowadzenie, Studia z Automatyki i Informatyki, Tom 37, 2012, pp.79-94.
  • [12] Liu J.S., Chen R., Sequential Monte Carlo Methods for Dynamic Systems, Journal of the American Statistical Association, September 1998, Vol. 93, No. 443, pp. 1032-1044.
  • [13] Mountney J., Obeid I., Silage D., Modular Particle Filtering FPGA Hardware Architecture for Brain Machine Interfaces, Conf Proc IEEE Eng Med Biol Soc. 2011, pp. 4617-4620.
  • [14] Pitt M., Shephard N., Filtering via simulation: auxiliary particle filters, Journal of the American Statistical association, Vol.94, No.446, pp.590-599.
  • [15] Schön T.B., Wills A., Ninness B., System identification of nonlinear state-space models, Automatica 47 (2011), pp.39-49.
  • [16] Sutharsan S., Kirubarajan T., Lang T., McDonald M., An Optimization-Based Parallel Particle Filter for Multitarget Tracking, IEEE Transactions on Aerospace and Electronic Systems, Vol.48, No.2, 4/2012, pp. 1601-1618.
  • [17] Thrun S., Particle Filters in Robotics, Proceedings of the 17th Annual Conference on Uncertainty in AI (UAI), 2002.
  • [18] Woo J., Kim Y-J., Lee J., Lim M-T., Localization of Mobile Robot using Particle Filter, SICE-ICASE International Joint Conference 2006, pp. 3031-3034.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-484651b5-7c3e-479c-a051-eb96027f859c
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