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Proceedings Paper

Classification of ground vehicles from acoustic data using fuzzy logic rule-based classifiers: early results
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Paper Abstract

There are lots of uncertainties with ground vehicle classification when such a classification is based only on acoustic emissions. To handle such uncertainties, we apply type-2 fuzzy system theoreies to the designs of FL rule-based vehicle classifiers, using amplitudes of the 2nd through 12th harmonics as the features. Statistical analysis of these features for both tracked and wheeled vehicles demonstrates that their standard deviations vary as much as their means; hence, the membership functions for the antecedents of the type-2 FL rule-based classifier were chosen to be Gaussian primary memberships with uncertain means of standard deviations. We constructed three classifiers for tracked/wheeled vehicle classification, namely Bayesian, type-1 and type-2 FL rule-based, and used the leave-one-out scheme to evaluate these classifiers. Our experiments demonstrated that the average false alarm rates of the type-1 and type-2 FL rule-based classifiers are much smaller than that of the Bayesian classifier; and the average false alarm rate of teh type-2 FL rule-based classifier is smaller than that of the type-1 FL rule-based classifier.

Paper Details

Date Published: 7 August 2002
PDF: 11 pages
Proc. SPIE 4743, Unattended Ground Sensor Technologies and Applications IV, (7 August 2002); doi: 10.1117/12.447466
Show Author Affiliations
Hongwei Wu, Univ. of Southern California (United States)
Jerry M. Mendel, Univ. of Southern California (United States)

Published in SPIE Proceedings Vol. 4743:
Unattended Ground Sensor Technologies and Applications IV
Edward M. Carapezza, Editor(s)

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