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Proceedings Paper

Evaluating three types of artificial neural networks for classifying vehicles with multisensor data
Author(s): William M. Crocoll; Newton C. Ellis; Dick B. Simmons
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Paper Abstract

A neural network methodology was developed and evaluated for performing vehicle classification with multisensor data collected from simulated unattended ground based sensors. A high fidelity computer simulation, called Sensor Evaluation Model (SENSEM), was used to create a battlefield scenario realistically simulating sensor-vehicle interactions consisting of three sensor types and four vehicle types. Data generated from sensor-vehicle interactions were preprocessed and used to train and test three types of neural networks. Backpropagation, probabilistic, and radial basis function networks were evaluated to determine differential performance effectiveness under various training conditions. Optimal network designs were experimentally determined for each network type for each training condition. An overall comparative analysis of classification accuracy and computational efficiency was then conducted to evaluate the performance between the network types' optimal network designs for each training condition. Results showed extremely high accuracy and rapid training and test times for all optimal network designs. Results also showed optimal network performance varied as a function of training conditions facilitating specification of the most effective neural network paradigm under certain conditions. The results of this research support the conclusion that neural network techniques can be applied successfully as part of an analysis subsystem for classifying vehicles with multisensor data obtained from unattended ground based sensors.

Paper Details

Date Published: 4 April 1997
PDF: 12 pages
Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); doi: 10.1117/12.271491
Show Author Affiliations
William M. Crocoll, U.S. Army Logistics Management College (United States)
Newton C. Ellis, Texas A&M Univ. (United States)
Dick B. Simmons, Texas A&M Univ. (United States)

Published in SPIE Proceedings Vol. 3077:
Applications and Science of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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