Design Method of High-Order Kalman Filter for Strong Nonlinear System Based on Kronecker Product Transform

In this paper, a novel design idea of high-order Kalman filter based on Kronecker product transform is proposed for a class of strong nonlinear stochastic dynamic systems. Firstly, those augmenting systems are modeled with help of the Kronecker product without system noise. Secondly, the augmented s...

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Main Authors: Xiaohan Liu, Chenglin Wen, Xiaohui Sun
Format: Article
Language:English
Published: MDPI AG 2022-01-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/22/2/653
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author Xiaohan Liu
Chenglin Wen
Xiaohui Sun
author_facet Xiaohan Liu
Chenglin Wen
Xiaohui Sun
author_sort Xiaohan Liu
collection DOAJ
description In this paper, a novel design idea of high-order Kalman filter based on Kronecker product transform is proposed for a class of strong nonlinear stochastic dynamic systems. Firstly, those augmenting systems are modeled with help of the Kronecker product without system noise. Secondly, the augmented system errors are illustratively charactered by Gaussian white noise. Thirdly, at the expanded space a creative high-order Kalman filter is delicately designed, which consists of high-order Taylor expansion, introducing magical intermediate variables, representing linear systems converted from strongly nonlinear systems, designing Kalman filter, etc. The performance of the proposed filter will be much better than one of EKF, because it uses more information than EKF. Finally, its promise is verified through commonly used digital simulation examples.
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spelling doaj.art-8e27c9ce2c13410799150101cd916f6e2023-11-23T15:22:15ZengMDPI AGSensors1424-82202022-01-0122265310.3390/s22020653Design Method of High-Order Kalman Filter for Strong Nonlinear System Based on Kronecker Product TransformXiaohan Liu0Chenglin Wen1Xiaohui Sun2School of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Automation, Guangdong University of Petrochemical Technology, Maoming 525000, ChinaSchool of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaIn this paper, a novel design idea of high-order Kalman filter based on Kronecker product transform is proposed for a class of strong nonlinear stochastic dynamic systems. Firstly, those augmenting systems are modeled with help of the Kronecker product without system noise. Secondly, the augmented system errors are illustratively charactered by Gaussian white noise. Thirdly, at the expanded space a creative high-order Kalman filter is delicately designed, which consists of high-order Taylor expansion, introducing magical intermediate variables, representing linear systems converted from strongly nonlinear systems, designing Kalman filter, etc. The performance of the proposed filter will be much better than one of EKF, because it uses more information than EKF. Finally, its promise is verified through commonly used digital simulation examples.https://www.mdpi.com/1424-8220/22/2/653Kronecker producthigh-order Taylor expansionKalman filternonlinear system
spellingShingle Xiaohan Liu
Chenglin Wen
Xiaohui Sun
Design Method of High-Order Kalman Filter for Strong Nonlinear System Based on Kronecker Product Transform
Sensors
Kronecker product
high-order Taylor expansion
Kalman filter
nonlinear system
title Design Method of High-Order Kalman Filter for Strong Nonlinear System Based on Kronecker Product Transform
title_full Design Method of High-Order Kalman Filter for Strong Nonlinear System Based on Kronecker Product Transform
title_fullStr Design Method of High-Order Kalman Filter for Strong Nonlinear System Based on Kronecker Product Transform
title_full_unstemmed Design Method of High-Order Kalman Filter for Strong Nonlinear System Based on Kronecker Product Transform
title_short Design Method of High-Order Kalman Filter for Strong Nonlinear System Based on Kronecker Product Transform
title_sort design method of high order kalman filter for strong nonlinear system based on kronecker product transform
topic Kronecker product
high-order Taylor expansion
Kalman filter
nonlinear system
url https://www.mdpi.com/1424-8220/22/2/653
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AT chenglinwen designmethodofhighorderkalmanfilterforstrongnonlinearsystembasedonkroneckerproducttransform
AT xiaohuisun designmethodofhighorderkalmanfilterforstrongnonlinearsystembasedonkroneckerproducttransform