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Deep wavelet scattering features for infrasonic threat identification
Author(s): Kaylen J. Bryan; Kaleb E. Smith; Mitchell L. Solomon; Dean A. Clauter; Anthony O. Smith; Adrian M. Peter
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Paper Abstract

Infrasonic waves continue to be a staple of threat identification due to their presence in a variety of natural and man-made events, along with their low-frequency characteristics supporting detection over great distances. Considering the large set of phenomena that produce infrasound, it is critical to develop methodologies that exploit the unique signatures generated by such events to aid in threat identification. In this work, we propose a new infrasonic time-series classification technique based on the recently introduced Wavelet Scattering Transform (WST). Leveraging concepts from wavelet theory and signal processing, the WST induces a deep feature mapping on time series that is locally time invariant and stable to time-warping deformations through cascades of signal filtering and modulus operators. We demonstrate that the WST features can be utilized with a variety of classification methods to gain better discrimination. Experimental validation on the Library of Typical Infrasonic Signals (LOTIS)—containing infrasound events from mountain associated waves, microbaroms, internal atmospheric gravity waves and volcanic eruptions—illustrates the effectiveness of our approach and demonstrate it to be competitive with other state-of-the-art classification techniques.

Paper Details

Date Published: 16 May 2018
PDF: 11 pages
Proc. SPIE 10629, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XIX, 1062918 (16 May 2018); doi: 10.1117/12.2304544
Show Author Affiliations
Kaylen J. Bryan, Florida Institute of Technology (United States)
Kaleb E. Smith, Florida Institute of Technology (United States)
Mitchell L. Solomon, Florida Institute of Technology (United States)
Dean A. Clauter, Air Force Technical Applications Ctr. (United States)
Anthony O. Smith, Florida Institute of Technology (United States)
Adrian M. Peter, Florida Institute of Technology (United States)

Published in SPIE Proceedings Vol. 10629:
Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XIX
Jason A. Guicheteau; Augustus Way Fountain; Chris R. Howle, Editor(s)

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