14th IFAC Symposium on System Identification, SYSID 2006

SYSID-2006 Paper Abstract


Paper FrA1.3

Straka, Ondrej (Univ. of West Bohemia in Pilsen), Simandl, Miroslav (Univ. of West Bohemia in Pilsen)

Adaptive Particle Filter Based on Fixed Efficient Sample Size

Scheduled for presentation during the Regular Session "Identification and Filtering of Nonlinear Systems" (FrA1), Friday, March 31, 2006, 11:10−11:30, Concert Hall

14th IFAC Symposium on System Identification, March 29 - 31, 2006, Newcastle, Australia

This information is tentative and subject to change. Compiled on July 16, 2018

Keywords Particle Filtering/Monte Carlo Methods, Bayesian Methods


The paper deals with the particle filter in state estimation of a discretetime nonlinear nongaussian system. The aim of the paper is to design a sample size adaptation technique to guarantee an estimate quality. The proposed sample size adaptation technique considers an unadapted particle filter with a fixed number of samples that would be drawn directly from the filtering probability density function and modifies the sample size of the adapted particle filter to keep the particle filters estimate quality identical. The adaptation technique is based on the effective sample size and utilizes the sampling probability density function and an implicit form of the filtering probability density function. Application of the particle filter with the sample size adaptation technique is illustrated in a numerical example.