An Improved Unscented Kalman Filter for Discrete Nonlinear Systems with Random Parameters

An Improved Unscented Kalman Filter for Discrete Nonlinear Systems with Random Parameters

This paper investigates the nonlinear unscented Kalman filtering (UKF) problem for discrete nonlinear dynamic systems with random parameters. We develop an improved unscented transformation by incorporating the random parameters into the state vector to enlarge the number of sigma points. The theore...

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Main Authors: Yue Wang, Zhijian Qiu, Xiaomei Qu
Format: Article
Language:English
Published: Hindawi Limited 2017-01-01
Series:Discrete Dynamics in Nature and Society
Online Access:http://dx.doi.org/10.1155/2017/7905690
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spelling doaj-a677c655c2aa491bbf60c3bf5bef7cc12020-11-24T20:59:01ZengHindawi LimitedDiscrete Dynamics in Nature and Society1026-02261607-887X2017-01-01201710.1155/2017/79056907905690An Improved Unscented Kalman Filter for Discrete Nonlinear Systems with Random ParametersYue Wang0Zhijian Qiu1Xiaomei Qu2School of Economic Mathematics, Southwestern University of Finance and Economics, Chengdu, Sichuan 611130, ChinaSchool of Economic Mathematics, Southwestern University of Finance and Economics, Chengdu, Sichuan 611130, ChinaCollege of Computer Science and Technology, Southwest University for Nationalities, Chengdu, Sichuan 610041, ChinaThis paper investigates the nonlinear unscented Kalman filtering (UKF) problem for discrete nonlinear dynamic systems with random parameters. We develop an improved unscented transformation by incorporating the random parameters into the state vector to enlarge the number of sigma points. The theoretical analysis reveals that the approximated mean and covariance via the improved unscented transformation match the true values correctly up to the third order of Taylor series expansion. Based on the improved unscented transformation, an improved UKF method is proposed to expand the application of the UKF for nonlinear systems with random parameters. An application to the mobile source localization with time difference of arrival (TDOA) measurements and sensor position uncertainties is provided where the simulation results illustrate that the improved UKF method leads to a superior performance in comparison with the normal UKF method.http://dx.doi.org/10.1155/2017/7905690
collection DOAJ
language English
format Article
sources DOAJ
author Yue Wang
Zhijian Qiu
Xiaomei Qu
spellingShingle Yue Wang
Zhijian Qiu
Xiaomei Qu
An Improved Unscented Kalman Filter for Discrete Nonlinear Systems with Random Parameters
Discrete Dynamics in Nature and Society
author_facet Yue Wang
Zhijian Qiu
Xiaomei Qu
author_sort Yue Wang
title An Improved Unscented Kalman Filter for Discrete Nonlinear Systems with Random Parameters
title_short An Improved Unscented Kalman Filter for Discrete Nonlinear Systems with Random Parameters
title_full An Improved Unscented Kalman Filter for Discrete Nonlinear Systems with Random Parameters
title_fullStr An Improved Unscented Kalman Filter for Discrete Nonlinear Systems with Random Parameters
title_full_unstemmed An Improved Unscented Kalman Filter for Discrete Nonlinear Systems with Random Parameters
title_sort improved unscented kalman filter for discrete nonlinear systems with random parameters
publisher Hindawi Limited
series Discrete Dynamics in Nature and Society
issn 1026-0226
1607-887X
publishDate 2017-01-01
description This paper investigates the nonlinear unscented Kalman filtering (UKF) problem for discrete nonlinear dynamic systems with random parameters. We develop an improved unscented transformation by incorporating the random parameters into the state vector to enlarge the number of sigma points. The theoretical analysis reveals that the approximated mean and covariance via the improved unscented transformation match the true values correctly up to the third order of Taylor series expansion. Based on the improved unscented transformation, an improved UKF method is proposed to expand the application of the UKF for nonlinear systems with random parameters. An application to the mobile source localization with time difference of arrival (TDOA) measurements and sensor position uncertainties is provided where the simulation results illustrate that the improved UKF method leads to a superior performance in comparison with the normal UKF method.
url http://dx.doi.org/10.1155/2017/7905690
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AT xiaomeiqu animprovedunscentedkalmanfilterfordiscretenonlinearsystemswithrandomparameters
AT yuewang improvedunscentedkalmanfilterfordiscretenonlinearsystemswithrandomparameters
AT zhijianqiu improvedunscentedkalmanfilterfordiscretenonlinearsystemswithrandomparameters
AT xiaomeiqu improvedunscentedkalmanfilterfordiscretenonlinearsystemswithrandomparameters
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