Publication detail

Estimation of parameters of one-step predictor with particle filter method

LEBEDA, A.

Original Title

Estimation of parameters of one-step predictor with particle filter method

English Title

Estimation of parameters of one-step predictor with particle filter method

Type

conference paper

Language

en

Original Abstract

This paper is focused on estimation of the parameters of a system with non-Gaussian noise. Firstly, the Bayesian inference is described and the method of the particle filters is introduced which is directly based on the Bayesian inference. The particle filters method numrically solve a problem of a recursive Bayesian state estimator. Secondly, the method for transformation of a random variables is introduced which changes the relative likelihood of the particle filters according to the distribution of the measurement noise. Thirdly, recursive least square method is derived and linear one-step predictor is described. Fourthly, parameters of the one-step predictor are estimated online with two methods that were mention before. The outputs of both methods are compared and results are discussed. The particle filters method with random variables is analyzed.

English abstract

This paper is focused on estimation of the parameters of a system with non-Gaussian noise. Firstly, the Bayesian inference is described and the method of the particle filters is introduced which is directly based on the Bayesian inference. The particle filters method numrically solve a problem of a recursive Bayesian state estimator. Secondly, the method for transformation of a random variables is introduced which changes the relative likelihood of the particle filters according to the distribution of the measurement noise. Thirdly, recursive least square method is derived and linear one-step predictor is described. Fourthly, parameters of the one-step predictor are estimated online with two methods that were mention before. The outputs of both methods are compared and results are discussed. The particle filters method with random variables is analyzed.

Keywords

particle filters, non-Gaussian, Bayessian inference, identification, linear model

RIV year

2015

Released

13.05.2015

Publisher

Silesian University of Technology, Poland

Location

Cracow, Poland

ISBN

1474-6670

Periodical

Programmable devices and systems

Year of study

2015

Number

13

State

GB

Pages from

256

Pages to

261

Pages count

6

URL

Documents

BibTex


@inproceedings{BUT115314,
  author="Aleš {Lebeda}",
  title="Estimation of parameters of one-step predictor with particle filter method",
  annote="This paper is focused on estimation of the parameters of a system with non-Gaussian noise. Firstly, the Bayesian inference is described and the method of the particle filters is introduced which is directly based on the Bayesian inference. The particle filters method numrically solve a problem of a recursive Bayesian state estimator. Secondly, the method for transformation of a random variables is introduced which changes the relative likelihood of the particle filters according to the distribution of the measurement noise. Thirdly, recursive least square method is derived and linear one-step predictor is described. Fourthly, parameters of the one-step predictor are estimated online with two methods that were mention before. The outputs of both methods are compared and results are discussed. The particle filters method with random variables is analyzed.",
  address="Silesian University of Technology, Poland",
  booktitle="13th IFAC Conference on Programmable Devices and Embedded Systems - PDeS 2015",
  chapter="115314",
  doi="10.1016/j.ifacol.2015.07.043",
  howpublished="online",
  institution="Silesian University of Technology, Poland",
  number="13",
  year="2015",
  month="may",
  pages="256--261",
  publisher="Silesian University of Technology, Poland",
  type="conference paper"
}