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Proceedings Paper

Detection and classification of indoor objects using acoustic excitations
Author(s): Pawan Setlur; Moeness G. Amin; Abdelhak M. Zoubir
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Paper Abstract

In this paper, we show that objects of interest, like pipes and cylinders, reminiscent of guns and rifles, can be classified based on their acoustic vibration signatures. That is, if the acoustic returns are measurable, one can indeed classify objects based on the physical principle of resonance. We consider classifiers which are both training independent and those that are training dependent. The statistical classifier belongs to the former category, whereas, the neural network classifier belongs to the latter. Comparisons between the two approaches are shown to render both classifiers as suitable classifiers with small classification errors. We use the probability of correct classification as a measure of performance. We demonstrate experimentally that unique features for classification are the resonant frequencies. The measured data are obtained by exciting mechanical vibrations in pipes of different lengths and of different metals, for example, copper, aluminum, and steel, and the measuring of the acoustic returns, using a simple microphone. Autoregressive modeling is applied to the data to extract the respective object features, namely, the vibration frequencies and damping values. We consider two classification problems, 1) Classifying objects comprised of different metals, and 2) Classifying objects of the same material, but made of different lengths. It is shown that classification performance can be improved by incorporating additional features such as the damping coefficients.

Paper Details

Date Published: 5 May 2009
PDF: 12 pages
Proc. SPIE 7305, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense VIII, 730514 (5 May 2009); doi: 10.1117/12.819124
Show Author Affiliations
Pawan Setlur, Villanova Univ. (United States)
Moeness G. Amin, Villanova Univ. (United States)
Abdelhak M. Zoubir, Darmstadt Univ. of Technology (Germany)


Published in SPIE Proceedings Vol. 7305:
Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense VIII
Edward M. Carapezza, Editor(s)

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