Associated neural network independent component analysis structure

Keehoon Kim; Andrew Kostrzweski

doi:10.1117/12.668517

2 May 2006 Associated neural network independent component analysis structure

Keehoon Kim, Andrew Kostrzweski

Proceedings Volume 6231, Unattended Ground, Sea, and Air Sensor Technologies and Applications VIII; 62310J (2006) https://doi.org/10.1117/12.668517
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

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%.

Citation Download Citation

Keehoon Kim and Andrew Kostrzweski "Associated neural network independent component analysis structure", Proc. SPIE 6231, Unattended Ground, Sea, and Air Sensor Technologies and Applications VIII, 62310J (2 May 2006); https://doi.org/10.1117/12.668517

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