Linearized Adaptive Filter for a Nonlinear System Using Neural Network Based on the Extended Kalman Filter

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  • 拡張Kalmanフィルタに基づいたニューラルネットによる非線形システムに対する線形近似適応フィルタ
  • カクチョウ Kalman フィルタ ニ モトズイタ ニューラル ネット ニヨル

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Abstract

The extended Kalman filter (EKF) is an approximate filter for nonlinear systems. The EKF is well used for the adaptive filtering problems which identify both the system states and parameters for linear systems. For nonlinear systems, the adaptive filer could not work well because the linear filter could not identify the nonliear characteristics. In this paper, we propose a new adaptive filter for nonlinear systems using a neural network based on the EKF. First, the linear filter with EKF is extended to an approximately linearized adaptive filter for the nonlinear systems based on first-order linearization. Next, a multi-layered neural network is applied to the linearized adaptive filter. A transition matrix and an ovservation matrix of the system are required in order to solve the filtering algorithms. We derive the elements of the transition matrix and the ovservation matirix using the functions of the neural network. Finally, simulation results show that the proposed filter has good performances for the state estimation problems of the nonlinear systems.

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