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Proceedings Paper

A data-driven personnel detection scheme for indoor surveillance using seismic sensors
Author(s): Arun Subramanian; Satish G. Iyengar; Kishan G. Mehrotra; Chilukuri K. Mohan; Pramod K. Varshney; Thyagaraju Damarla
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Paper Abstract

This paper describes experiments and analysis of seismic signals in addressing the problem of personnel detection for indoor surveillance. Data was collected using geophones to detect footsteps from walking and running in indoor environments such as hallways. Our analysis of the data shows the significant presence of nonlinearity, when tested using the surrogate data method. This necessitates the need for novel detector designs that are not based on linearity assumptions. We present one such method based on empirical mode decomposition (EMD) and functional data analysis (FDA) and evaluate its applicability on our collected dataset.

Paper Details

Date Published: 5 May 2009
PDF: 11 pages
Proc. SPIE 7333, Unattended Ground, Sea, and Air Sensor Technologies and Applications XI, 733315 (5 May 2009); doi: 10.1117/12.820237
Show Author Affiliations
Arun Subramanian, Syracuse Univ. (United States)
Satish G. Iyengar, Syracuse Univ. (United States)
Kishan G. Mehrotra, Syracuse Univ. (United States)
Chilukuri K. Mohan, Syracuse Univ. (United States)
Pramod K. Varshney, Syracuse Univ. (United States)
Thyagaraju Damarla, Army Research Lab. (United States)


Published in SPIE Proceedings Vol. 7333:
Unattended Ground, Sea, and Air Sensor Technologies and Applications XI
Edward M. Carapezza, Editor(s)

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