Blind separation of human- and horse-footstep signatures using independent component analysis

Asif Mehmood; Thyagaraju Damarla

doi:10.1117/12.919577

7 May 2012 Blind separation of human- and horse-footstep signatures using independent component analysis

Asif Mehmood, Thyagaraju Damarla

Proceedings Volume 8382, Active and Passive Signatures III; 83820L (2012) https://doi.org/10.1117/12.919577
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

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.

Citation Download Citation

Asif Mehmood and Thyagaraju Damarla "Blind separation of human- and horse-footstep signatures using independent component analysis", Proc. SPIE 8382, Active and Passive Signatures III, 83820L (7 May 2012); https://doi.org/10.1117/12.919577

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