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Proceedings Paper

Applying matching pursuit decomposition time-frequency processing to UGS footstep classification
Author(s): Brett W. Larsen; Hugh Chung; Alfonso Dominguez; Jacob Sciacca; Narayan Kovvali; Antonia Papandreou-Suppappola; David R. Allee
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Paper 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.

Paper Details

Date Published: 6 June 2013
PDF: 13 pages
Proc. SPIE 8711, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense XII, 871104 (6 June 2013); doi: 10.1117/12.2015498
Show Author Affiliations
Brett W. Larsen, Arizona State Univ. (United States)
Hugh Chung, Arizona State Univ. (United States)
Alfonso Dominguez, Arizona State Univ. (United States)
Jacob Sciacca, Arizona State Univ. (United States)
Narayan Kovvali, Arizona State Univ. (United States)
Antonia Papandreou-Suppappola, Arizona State Univ. (United States)
David R. Allee, Arizona State Univ. (United States)


Published in SPIE Proceedings Vol. 8711:
Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense XII
Edward M. Carapezza, Editor(s)

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