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Proceedings Paper

Artificial neural system development for airborne acoustic signature classification
Author(s): Tomas F. Tarr; Ali Farsaie
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Paper Abstract

Classification of acoustic signatures of airborne targets using a Hybrid Artificial Neural System (ANS) is described in this paper. The acoustic data used is field data taken from various helicopters. Data used in this study was composed of multiple classes of helicopter signatures, each having several time-series segments. Test results indicate greater than 96 percent correct classification on multiple helicopter classes. The results also show that the ANS can generalize, when trained using reduced time-series segments sampled from original signatures of a target.

Paper Details

Date Published: 3 September 1993
PDF: 12 pages
Proc. SPIE 1955, Signal Processing, Sensor Fusion, and Target Recognition II, (3 September 1993); doi: 10.1117/12.154980
Show Author Affiliations
Tomas F. Tarr, Naval Surface Warfare Ctr. (United States)
Ali Farsaie, Naval Surface Warfare Ctr. (United States)

Published in SPIE Proceedings Vol. 1955:
Signal Processing, Sensor Fusion, and Target Recognition II
Ivan Kadar; Vibeke Libby, Editor(s)

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