@inproceedings{b39ffa722a3b4589b8e0d419b5d12bfb,
title = "A data-driven personnel detection scheme for indoor surveillance using seismic sensors",
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.",
keywords = "Empirical mode decomposition, Functional data analysis, Indoor surveillance, Seismic signal processing, Test for nonlinearity",
author = "Arun Subramanian and Iyengar, {Satish G.} and Mehrotra, {Kishan G.} and Mohan, {Chilukuri K.} and Varshney, {Pramod K.} and Thyagaraju Damarla",
note = "Copyright: Copyright 2009 Elsevier B.V., All rights reserved.; Unattended Ground, Sea, and Air Sensor Technologies and Applications XI ; Conference date: 13-04-2009 Through 16-04-2009",
year = "2009",
doi = "10.1117/12.820237",
language = "English (US)",
isbn = "9780819475992",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
booktitle = "Unattended Ground, Sea, and Air Sensor Technologies and Applications XI",
}