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Proceedings Paper

Target tracking with multipoint predictive neural network
Author(s): Gee-In Goo; Heather T. Goo
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Paper Abstract

Frequently, multipoint target tracking is achieved using a Kalman Filter or other means. Numerous papers have been published over the past decades on tracking of dynamic systems such as ships, planes, artillery shells, and control processes with Kalman Filters, particularly, when the mathematical equations of motion describing the dynamic system are available. Then, target tracking is a fairly straight forward procedure. In this paper, a back propagation neural network is successfully `trained' for tracking an artillery shell. It is a predictive neural network because its outputs are the future positions of the artillery shell.

Paper Details

Date Published: 16 September 1992
PDF: 12 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.140014
Show Author Affiliations
Gee-In Goo, Morgan State Univ. (United States)
Heather T. Goo, Univ. of Maryland/Baltimore (United States)

Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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