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Proceedings Paper

Associated neural network independent component analysis structure
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Paper Abstract

Detection, classification, and localization of potential security breaches in extremely high-noise environments are important for perimeter protection and threat detection both for homeland security and for military force protection. Physical Optics Corporation has developed a threat detection system to separate acoustic signatures from unknown, mixed sources embedded in extremely high-noise environments where signal-to-noise ratios (SNRs) are very low. Associated neural network structures based on independent component analysis are designed to detect/separate new acoustic sources and to provide reliability information. The structures are tested through computer simulations for each critical component, including a spontaneous detection algorithm for potential threat detection without a predefined knowledge base, a fast target separation algorithm, and nonparametric methodology for quantified confidence measure. The results show that the method discussed can separate hidden acoustic sources of SNR in 5 dB noisy environments with an accuracy of 80%.

Paper Details

Date Published: 2 May 2006
PDF: 7 pages
Proc. SPIE 6231, Unattended Ground, Sea, and Air Sensor Technologies and Applications VIII, 62310J (2 May 2006); doi: 10.1117/12.668517
Show Author Affiliations
Keehoon Kim, Physical Optics Corp. (United States)
Andrew Kostrzweski, Physical Optics Corp. (United States)

Published in SPIE Proceedings Vol. 6231:
Unattended Ground, Sea, and Air Sensor Technologies and Applications VIII
Edward M. Carapezza, Editor(s)

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