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Comparison of few particle filter varieties


[ 1 ] Wydział Elektryczny, Politechnika Poznańska | [ D ] phd student

Year of publication


Published in

Computer Applications in Electrical Engineering

Journal year: 2013 | Journal volume: vol. 11

Article type

scientific article

Publication language


  • particle filter
  • auxiliary
  • bootstrap
  • rao-blackwellised
  • sequential monte carlo

EN Particle filters are very popular - number of algorithms based on Sequential Monte Carlo methods is growing. Paper describes and compares the performance of four of them: Auxiliary Particle Filter - this approach should reduce the high sensitivity to outliers values and poor posterior approximation, Rao-Blackwellised Particle Filter - this approach is recommended for objects with linear and nonlinear state variables, Bootstrap Filter - the first proposed Particle Filter which still can be used, because is very simple to implement, and some variety of SIR algorithm - this algorithm was chosen to show, that importance density also can be constant. The obtained results show that Bootstrap Filter and Rao-Blackwellised approaches give good results, but Bootstrap Filter works 10 times faster. The worst results gives SIR algorithm with unconditional importance function.

Pages (from - to)

345 - 355

Ministry points / journal


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