Auxiliary models based multi-innovation gradient identification with colored measurement noises

Authors:
Feng Ding;Peter X. Liu;Guangjun Liu
Affiliations:
School of Communication and Control Engineering, Jiangnan University, Wuxi, China;Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada;Department of Aerospace Engineering, Ryerson University, Toronto, Canada
Venue:
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Year:
2009

Citing 4
Cited 0

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Abstract

For pseudo-linear regression identification models corresponding output error systems with colored measurement noises, a difficulty of identification is that there exist unknown inner variables and unmeasurable noise terms in the information vector. This paper presents an auxiliary model based multiinnovation stochastic gradient algorithm by using the auxiliary model technique and by expanding the scalar innovation to an innovation vector. Compared with single-innovation stochastic gradient algorithm, the proposed approach can generate highly accurate parameter estimates. The simulation results confirm theoretical findings.