NDLI: A 2-D intercept problem using the neural extended Kalman filter for tracking and linear predictions

Content Provider	IEEE Xplore Digital Library
Author	Stubberud, S.C. Kramer, K.A.
Copyright Year	2005
Description	Author affiliation: Naval Electron. & Navigation, Boeing Co., Anaheim, CA, USA (Stubberud, S.C.)
Abstract	The neural extended Kalman filter is a technique that learns unmodeled dynamics while performing state estimation in the feedback loop of a control system. This coupled system performs the standard estimation of the states of the plant while estimating a function to approximate the difference between the given state-coupling function model and the behavior of the true plant dynamics. At each sample step, this new model is added to the existing model to improve the state estimate. The neural extended Kalman filter is applied to a two-dimensional intercept problem and the results are compared to those obtained from a standard tracking system. Since the neural extended Kalman filter better models the dynamic system, the time prediction of the state estimate which is needed for intercept control is superior to that provided by a standard tracking model. In this paper, predictions of one, two, and five time steps are investigated for use as the reference signal.
Starting Page	367
Ending Page	372
File Size	700125
Page Count	6
File Format	PDF
ISBN	0780388089
ISSN	00942898
DOI	10.1109/SSST.2005.1460938
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-03-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Target tracking State estimation Neural networks Predictive models Control systems Motion estimation Navigation Kalman filters Feedback loop Robot kinematics
Content Type	Text
Resource Type	Article

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