23 May 2018 Near-field infrasound classification of rocket launch signatures
Author Affiliations +
Abstract
Discrimination between different rocket types is an important application for utilizing infrasound in event monitoring within a range of 0-100 km. This is in contrast to traditional nuclear weapons monitoring which leverages infrasound propagation over thousands of kilometers. The motivation of this research is to demonstrate the utilization of deep neural network architectures to discriminate infrasonic signals produced by rocket launches and collected by an near-field infrasound sensor array. The data collection contains three space bound rocket classes: Delta IV, Atlas V, and Falcon 9. In particular, we investigate the classification accuracy of a multi-class convolutional neural network (CNN) and a deep neural network (DNN) on various feature representations, such as neural network derived features, spectrograms, and wavelet scattering transform coefficients. Our experiments validate the viability of a CNN and DNN framework for near-field infrasonic applications, with our proposed method achieving favorable results.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kaleb E. Smith, Mitchell L. Solomon, Kaylen J. Bryan, Anthony O. Smith, Adrian M. Peter, "Near-field infrasound classification of rocket launch signatures", Proc. SPIE 10629, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XIX, 106291F (23 May 2018); doi: 10.1117/12.2302680; https://doi.org/10.1117/12.2302680
PROCEEDINGS
11 PAGES


SHARE
Back to Top