A data-driven personnel detection scheme for indoor surveillance using seismic sensors

Arun Subramanian, Satish G. Iyengar, Kishan G. Mehrotra, Chilukuri K. Mohan, Pramod K. Varshney, Thyagaraju Damarla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationUnattended Ground, Sea, and Air Sensor Technologies and Applications XI
DOIs
StatePublished - Sep 8 2009
EventUnattended Ground, Sea, and Air Sensor Technologies and Applications XI - Orlando, FL, United States
Duration: Apr 13 2009Apr 16 2009

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume7333
ISSN (Print)0277-786X

Other

OtherUnattended Ground, Sea, and Air Sensor Technologies and Applications XI
CountryUnited States
CityOrlando, FL
Period4/13/094/16/09

Keywords

  • Empirical mode decomposition
  • Functional data analysis
  • Indoor surveillance
  • Seismic signal processing
  • Test for nonlinearity

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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