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Proceedings Paper

Time difference of arrival to blast localization of potential chemical/biological event on the move
Author(s): Amir Morcos; Sachi Desai; Brian Peltzer; Myron E. Hohil
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Paper Abstract

Integrating a sensor suite with ability to discriminate potential Chemical/Biological (CB) events from high-explosive (HE) events employing a standalone acoustic sensor with a Time Difference of Arrival (TDOA) algorithm we developed a cueing mechanism for more power intensive and range limited sensing techniques. Enabling the event detection algorithm to locate to a blast event using TDOA we then provide further information of the event as either Launch/Impact and if CB/HE. The added information is provided to a range limited chemical sensing system that exploits spectroscopy to determine the contents of the chemical event. The main innovation within this sensor suite is the system will provide this information on the move while the chemical sensor will have adequate time to determine the contents of the event from a safe stand-off distance. The CB/HE discrimination algorithm exploits acoustic sensors to provide early detection and identification of CB attacks. Distinct characteristics arise within the different airburst signatures because HE warheads emphasize concussive and shrapnel effects, while CB warheads are designed to disperse their contents over large areas, therefore employing a slower burning, less intense explosive to mix and spread their contents. Differences characterized by variations in the corresponding peak pressure and rise time of the blast, differences in the ratio of positive pressure amplitude to the negative amplitude, and variations in the overall duration of the resulting waveform. The discrete wavelet transform (DWT) is used to extract the predominant components of these characteristics from air burst signatures at ranges exceeding 3km. Highly reliable discrimination is achieved with a feed-forward neural network classifier trained on a feature space derived from the distribution of wavelet coefficients and higher frequency details found within different levels of the multiresolution decomposition. The development of an adaptive noise floor to provide early event detection assists in minimizing the false alarm rate and increasing the confidence whether the event is blast event or back ground noise. The integration of these algorithms with the TDOA algorithm provides a complex suite of algorithms that can give early warning detection and highly reliable look direction from a great stand-off distance for a moving vehicle to determine if a candidate blast event is CB and if CB what is the composition of the resulting cloud.

Paper Details

Date Published: 15 October 2007
PDF: 13 pages
Proc. SPIE 6736, Unmanned/Unattended Sensors and Sensor Networks IV, 67360K (15 October 2007); doi: 10.1117/12.738189
Show Author Affiliations
Amir Morcos, U.S. Army Research, Development and Engineering Command (United States)
Sachi Desai, U.S. Army Research, Development and Engineering Command (United States)
Brian Peltzer, U.S. Army Research, Development and Engineering Command (United States)
Myron E. Hohil, 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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