DocumentCode :
3485982
Title :
A 2-D intercept problem using the neural extended Kalman filter for tracking and linear predictions
Author :
Stubberud, Stephen C. ; Kramer, Kathleen A.
Author_Institution :
Naval Electron. & Navigation, Boeing Co., Anaheim, CA, USA
fYear :
2005
fDate :
20-22 March 2005
Firstpage :
367
Lastpage :
372
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.
Keywords :
Kalman filters; closed loop systems; feedback; motion control; neural nets; prediction theory; state estimation; target tracking; feedback loop; intercept control; linear predictions; neural extended Kalman filter; reference signal; state estimation; state-coupling function model; target tracking; two-dimensional intercept problem; unmodeled dynamics; Control systems; Feedback loop; Kalman filters; Motion estimation; Navigation; Neural networks; Predictive models; Robot kinematics; State estimation; Target tracking;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
System Theory, 2005. SSST '05. Proceedings of the Thirty-Seventh Southeastern Symposium on
ISSN :
0094-2898
Print_ISBN :
0-7803-8808-9
Type :
conf
DOI :
10.1109/SSST.2005.1460938
Filename :
1460938
Link To Document :
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