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

Hongwei Wu; Jerry M. Mendel

doi:10.1117/12.447466

7 August 2002 Classification of ground vehicles from acoustic data using fuzzy logic rule-based classifiers: early results

Hongwei Wu, Jerry M. Mendel

Author Affiliations +

Proceedings Volume 4743, Unattended Ground Sensor Technologies and Applications IV; (2002) https://doi.org/10.1117/12.447466
Event: AeroSense 2002, 2002, Orlando, FL, United States

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.

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Hongwei Wu and Jerry M. Mendel "Classification of ground vehicles from acoustic data using fuzzy logic rule-based classifiers: early results", Proc. SPIE 4743, Unattended Ground Sensor Technologies and Applications IV, (7 August 2002); https://doi.org/10.1117/12.447466

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