Research

 

State space second order filter estimation

 

J.J. Medelª and M. T. Zagaceta^b

 

^a Computer Research Centre, Venus S/N, Col. Nueva Industrial Vallejo, 07738.

^b Mechanical and Engineering School, Av. De las Granjas N.- 682 Col. Santa Catarina, e-mail: jjmedelj@yahoo.com.mx; mtza79@yahoo.com.mx

 

Received 9 April 2012;
Accepted 26 February 2013

 

Abstract

The second order stochastic filter is based on difference models with uncorrelated innovation conditions structured in state space having stationary properties through a surface with bounded drift around the mean value. This allows building recursive estimation without generality lost and basic properties over the stochastic state space surface with unknown gains viewed as a black-box scheme. The spatial region generated gave an approximation to real parametres set with a sufficient convergence rate in a probability sense. The results were applied in adaptive identification states with a high convergence rate, observed in the functional error described illustratively in simulations. This technique was developed over the smooth slide surface having advantages over other traditional filters.

Keywords: State space estimation; least squares method; instrumental variable; second probability moment convergence rate.

 

PACS: 02.50.-r; 02.50.Ey; 02.70Bf; 02.70.-c.

 

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