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Proceedings Paper

Mortar and artillery variants classification by exploiting characteristics of the acoustic signature
Author(s): Myron E. Hohil; David Grasing; Sachi Desai; Amir Morcos
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Paper Abstract

Feature extraction methods based on the discrete wavelet transform and multiresolution analysis facilitate the development of a robust classification algorithm that reliably discriminates mortar and artillery variants via acoustic signals produced during the launch/impact events. Utilizing acoustic sensors to exploit the sound waveform generated from the blast for the identification of mortar and artillery variants. Distinct characteristics arise within the different mortar variants because varying HE mortar payloads and related charges emphasize concussive and shrapnel effects upon impact employing varying magnitude explosions. The different mortar variants are characterized by variations in the resulting waveform of the event. The waveform holds various harmonic properties distinct to a given mortar/artillery variant that through advanced signal processing techniques can employed to classify a given set. The DWT and other readily available signal processing techniques will be used to extract the predominant components of these characteristics from the acoustic signatures at ranges exceeding 2km. Exploiting these techniques will help develop a feature set highly independent of range, providing discrimination based on acoustic elements of the blast wave. Highly reliable discrimination will be achieved with a feed-forward neural network classifier trained on a feature space derived from the distribution of wavelet coefficients, frequency spectrum, and higher frequency details found within different levels of the multiresolution decomposition. The process that will be described herein extends current technologies, which emphasis multi modal sensor fusion suites to provide such situational awareness. A two fold problem of energy consumption and line of sight arise with the multi modal sensor suites. The process described within will exploit the acoustic properties of the event to provide variant classification as added situational awareness to the solider.

Paper Details

Date Published: 15 October 2007
PDF: 11 pages
Proc. SPIE 6736, Unmanned/Unattended Sensors and Sensor Networks IV, 67360J (15 October 2007); doi: 10.1117/12.738185
Show Author Affiliations
Myron E. Hohil, U.S. Army Research, Development and Engineering Command (United States)
David Grasing, U.S. Army Research, Development and Engineering Command (United States)
Sachi Desai, U.S. Army Research, Development and Engineering Command (United States)
Amir Morcos, U.S. Army Research, Development and Engineering Command (United States)


Published in SPIE Proceedings Vol. 6736:
Unmanned/Unattended Sensors and Sensor Networks IV
Edward M. Carapezza, Editor(s)

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