Parameter identification of nonlinear system and its application based on strong tracking filter and wavelet transform

Jie Chen; Fang Deng; Wen Jie Chen; Tao Ma

Parameter identification of nonlinear system and its application based on strong tracking filter and wavelet transform

Jie Chen, Fang Deng^*, Wen Jie Chen, Tao Ma

^*Corresponding author for this work

School of Automation

Beijing Institute of Technology

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

The strong tracking extended Kalman filter (STEKF) is used as the main frame and the linearization and state expansion are employed to estimate the time-varying parameters and states of nonlinear systems. Based on the general STEKF, a wavelet-transform-based filter is proposed to estimate the variance of the measurement noise, and a new filtering gain factor is utilized in STEKF to eliminate the tracking overshoot. Main formulas for calculation and the methods for selecting parameters are presented. Monte Carlo simulation and practical application in identification of ballistic parameters demonstrate that the proposed method can exactly estimate the abruptly changing parameters even when the variance of the measurement noise is time-varying. The estimation accuracy of parameters and states is higher than that of the general STEKF.

Original language	English
Pages (from-to)	738-744
Number of pages	7
Journal	Kongzhi Lilun Yu Yinyong/Control Theory and Applications
Volume	27
Issue number	6
Publication status	Published - Jun 2010

Keywords

Nonlinear systems
Parameter identification
Strong tracking filter
Wavelet transform

Cite this

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title = "Parameter identification of nonlinear system and its application based on strong tracking filter and wavelet transform",

abstract = "The strong tracking extended Kalman filter (STEKF) is used as the main frame and the linearization and state expansion are employed to estimate the time-varying parameters and states of nonlinear systems. Based on the general STEKF, a wavelet-transform-based filter is proposed to estimate the variance of the measurement noise, and a new filtering gain factor is utilized in STEKF to eliminate the tracking overshoot. Main formulas for calculation and the methods for selecting parameters are presented. Monte Carlo simulation and practical application in identification of ballistic parameters demonstrate that the proposed method can exactly estimate the abruptly changing parameters even when the variance of the measurement noise is time-varying. The estimation accuracy of parameters and states is higher than that of the general STEKF.",

keywords = "Nonlinear systems, Parameter identification, Strong tracking filter, Wavelet transform",

author = "Jie Chen and Fang Deng and Chen, {Wen Jie} and Tao Ma",

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journal = "Kongzhi Lilun Yu Yinyong/Control Theory and Applications",

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T1 - Parameter identification of nonlinear system and its application based on strong tracking filter and wavelet transform

AU - Chen, Jie

AU - Deng, Fang

AU - Chen, Wen Jie

AU - Ma, Tao

PY - 2010/6

Y1 - 2010/6

N2 - The strong tracking extended Kalman filter (STEKF) is used as the main frame and the linearization and state expansion are employed to estimate the time-varying parameters and states of nonlinear systems. Based on the general STEKF, a wavelet-transform-based filter is proposed to estimate the variance of the measurement noise, and a new filtering gain factor is utilized in STEKF to eliminate the tracking overshoot. Main formulas for calculation and the methods for selecting parameters are presented. Monte Carlo simulation and practical application in identification of ballistic parameters demonstrate that the proposed method can exactly estimate the abruptly changing parameters even when the variance of the measurement noise is time-varying. The estimation accuracy of parameters and states is higher than that of the general STEKF.

AB - The strong tracking extended Kalman filter (STEKF) is used as the main frame and the linearization and state expansion are employed to estimate the time-varying parameters and states of nonlinear systems. Based on the general STEKF, a wavelet-transform-based filter is proposed to estimate the variance of the measurement noise, and a new filtering gain factor is utilized in STEKF to eliminate the tracking overshoot. Main formulas for calculation and the methods for selecting parameters are presented. Monte Carlo simulation and practical application in identification of ballistic parameters demonstrate that the proposed method can exactly estimate the abruptly changing parameters even when the variance of the measurement noise is time-varying. The estimation accuracy of parameters and states is higher than that of the general STEKF.

KW - Nonlinear systems

KW - Parameter identification

KW - Strong tracking filter

KW - Wavelet transform

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SN - 1000-8152

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JO - Kongzhi Lilun Yu Yinyong/Control Theory and Applications

JF - Kongzhi Lilun Yu Yinyong/Control Theory and Applications

IS - 6

ER -

Parameter identification of nonlinear system and its application based on strong tracking filter and wavelet transform

Abstract

Keywords

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