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Proceedings Paper

Blind separation of human- and horse-footstep signatures using independent component analysis
Author(s): Asif Mehmood; Thyagaraju Damarla
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Paper Abstract

Seismic footstep detection based systems for homeland security applications are important to perimeter protection and other security systems. This paper reports seismic footstep signal separation for a walking horse and a walking human. The well-known Independent Component Analysis (ICA) approach is employed to accomplish this task. ICA techniques have become widely used in audio analysis and source separation. The concept of lCA may actually be seen as an extension of the principal component analysis (PCA), which can only impose independence up to the second order and, consequently, defines directions that are orthogonal. They can also be used in conjunction with a classification method to achieve a high percentage of correct classification and reduce false alarms. In this paper, an ICA based algorithm is developed and implemented on seismic data of human and horse footsteps. The performance of this method is very promising and is demonstrated by the experimental results.

Paper Details

Date Published: 7 May 2012
PDF: 7 pages
Proc. SPIE 8382, Active and Passive Signatures III, 83820L (7 May 2012); doi: 10.1117/12.919577
Show Author Affiliations
Asif Mehmood, U.S. Army Research Lab. (United States)
Thyagaraju Damarla, U.S. Army Research Lab. (United States)

Published in SPIE Proceedings Vol. 8382:
Active and Passive Signatures III
G. Charmaine Gilbreath; Chadwick Todd Hawley, Editor(s)

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