Auxiliary model based multi-innovation extended stochastic gradient parameter estimation with colored measurement noises

  • Authors:

  • Feng Ding;Peter X. Liu;Guangjun Liu

  • Affiliations:

  • School of Communication and Control Engineering, Jiangnan University, Wuxi 214122, China and Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada K1S 5B6;Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada K1S 5B6;Department of Aerospace Engineering, Ryerson University, Toronto, Canada M5B 2K3

  • Venue:
  • Signal Processing
  • Year:
  • 2009

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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 multi-innovation extended stochastic gradient algorithm by using the auxiliary model method and by expanding the scalar innovation to an innovation vector. Compared with single innovation extended stochastic gradient algorithm, the proposed approach can generate highly accurate parameter estimates. The simulation results confirm this conclusion.