Artificial neural system development for airborne acoustic signature classification

Tomas F. Tarr; Ali Farsaie

doi:10.1117/12.154980

3 September 1993 Artificial neural system development for airborne acoustic signature classification

Tomas F. Tarr, Ali Farsaie

Proceedings Volume 1955, Signal Processing, Sensor Fusion, and Target Recognition II; (1993) https://doi.org/10.1117/12.154980
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

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.

Citation Download Citation

Tomas F. Tarr and Ali Farsaie "Artificial neural system development for airborne acoustic signature classification", Proc. SPIE 1955, Signal Processing, Sensor Fusion, and Target Recognition II, (3 September 1993); https://doi.org/10.1117/12.154980

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