Further Investigation of a Gaussian Sum Filter Based on Stochastic Equivalent Linearization

Abstract

We proposed a stochastic (or statistical) Equivalent linearization - Gaussian Sum Filter(: EqGSFilter) for discrete time nonlinear Systems. Subsequently, in this paper, we investigate and showthe further results related to the EqGS Filter. Especially we discuss a method to apply Gauss-Hermite quadrature rules for evaluation of the conditional expected values of the quantities requiredto design the EqGS filter. Finally, we show the estimation results of AR modeling comparison withthe extended Kalman and the equivalent linearization filters.