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Joint parameter and state estimation based on particle filtering and stochastic approximation
Sydney Australia November 28-December 01
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CIMCA.2006.138International Conference on Computati ...
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Xiaojun Yang, Kunlin Shi, Keyi Xing, "Joint parameter and state estimation based on particle filtering and stochastic approximation," Computational Intelligence for Modelling, Control and Automation, International Conference on, pp. 42, International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA'06), 2006.
 
 
BibTex
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@article{ 10.1109/CIMCA.2006.138,
author = {Xiaojun Yang and Kunlin Shi and Keyi Xing},
title = {Joint parameter and state estimation based on particle filtering and stochastic approximation},
journal ={Computational Intelligence for Modelling, Control and Automation, International Conference on},
volume = {0},
year = {2006},
isbn = {0-7695-2731-0},
pages = {42},
doi = {http://doi.ieeecomputersociety.org/10.1109/CIMCA.2006.138},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Computational Intelligence for Modelling, Control and Automation, International Conference on
TI - Joint parameter and state estimation based on particle filtering and stochastic approximation
SN - 0-7695-2731-0
SP
EP
A1 - Xiaojun Yang,
A1 - Kunlin Shi,
A1 - Keyi Xing,
PY - 2006
KW - null
VL - 0
JA - Computational Intelligence for Modelling, Control and Automation, International Conference on
ER -
 
Xiaojun Yang, Xi?an Jiaotong University, Xi?an, China
Kunlin Shi, Xi?an Institute of Electromechanical Information Technology, Xi?an, China
Keyi Xing, Northwestern Polytechnical University, Xi?an, China
In this paper, an adaptive estimation algorithm is proposed for non-linear dynamic systems with unknown parameters based on combination of particle filtering and SPSA technique. The estimates of parameters are obtained by state samples and maximum-likelihood estimation under particle filtering, and the SPSA is used to approximate the gradient of target function. The proposed algorithm achieves joint estimation of dynamic state and static parameters. Simulation result demonstrates the efficiency of the algorithm.
Citation:
Xiaojun Yang, Kunlin Shi, Keyi Xing, "Joint parameter and state estimation based on particle filtering and stochastic approximation," cimca, pp.42, International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA'06), 2006
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