An improved tracking Kalman filter using a multilayered neural network

Authors:
K. Takaba;Y. Iiguni;H. Tokumaru
Affiliations:
Department of Applied Mathematics & Physics Faculty of Engineering, Kyoto University, Japan;Division of Applied Systems Science Faculty of Engineering, Kyoto University, Japan;Department of Computer Science and System Engineering Ritsumeikan University, Japan
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
1996

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Abstract

This paper presents a method for improving the estimation accuracy of a tracking Kalman filter (TKF) by using a multilayered neural network (MNN). Estimation accuracy of the TKF is degraded due to the uncertainties which cannot be expressed by the linear state-space model given a priori. The MNN capable of learning an arbitrary nonlinear mapping is thus added to the TKF to compensate the uncertainties. The MNN is trained so that it realizes a mapping from, the measurements to the corrections of estimations of the TKF. Simulation results show that the estimation accuracy is much improved by using the MNN.