Abstract

The challenge of rapid footstep detection and classification in remote locations has long been an important area of study for defense technology and national security. Also, as the military seeks to create effective and disposable unattended ground sensors (UGS), computational complexity and power consumption have become essential considerations in the development of classification techniques. In response to these issues, a research project at the Flexible Display Center at Arizona State University (ASU) has experimented with footstep classification using the matching pursuit decomposition (MPD) time-frequency analysis method. The MPD provides a parsimonious signal representation by iteratively selecting matched signal components from a pre-determined dictionary. The resulting time-frequency representation of the decomposed signal provides distinctive features for different types of footsteps, including footsteps during walking or running activities. The MPD features were used in a Bayesian classification method to successfully distinguish between the different activities. The computational cost of the iterative MPD algorithm was reduced, without significant loss in performance, using a modified MPD with a dictionary consisting of signals matched to cadence temporal gait patterns obtained from real seismic measurements. The classification results were demonstrated with real data from footsteps under various conditions recorded using a low-cost seismic sensor.

Original languageEnglish (US)
Title of host publicationSensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense XII
DOIs
StatePublished - Aug 9 2013
EventSensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense XII - Baltimore, MD, United States
Duration: Apr 29 2013May 1 2013

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume8711
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Other

OtherSensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense XII
CountryUnited States
CityBaltimore, MD
Period4/29/135/1/13

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Keywords

  • Bayesian classification
  • Feature extraction
  • Footstep classification
  • Matching pursuit decomposition
  • Seismic sensing
  • Time-frequency analysis
  • Unattended ground sensor

ASJC Scopus subject areas

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

Cite this

Larsen, B. W., Chung, H., Dominguez, A., Sciacca, J., Kovvali, N., Papandreou-Suppappola, A., & Allee, D. (2013). Applying matching pursuit decomposition time-frequency processing to UGS footstep classification. In Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense XII [871104] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 8711). https://doi.org/10.1117/12.2015498