Blind separation of multiple vehicle signatures in frequency domain

M. R. Azimi-Sadjadi; S. Srinivasan

doi:10.1117/12.610185

27 May 2005 Blind separation of multiple vehicle signatures in frequency domain

M. R. Azimi-Sadjadi, S. Srinivasan

Proceedings Volume 5796, Unattended Ground Sensor Technologies and Applications VII; (2005) https://doi.org/10.1117/12.610185
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

This paper considers the problem of classifying ground vehicles using their acoustic signatures recorded by unattended passive acoustic sensors. Using these sensors, acoustic signatures of a wide variety of sources such as trucks, tanks, personnel, and airborne targets can be recorded. Additionally, interference sources such as wind noise and ambient noise are typically present. The proposed approach in this paper relies on the blind source separation of the recorded signatures of various sources. Two different frequency domain source separation methods have been employed to separate the vehicle signatures that overlap both spectrally and temporally. These methods rely on the frequency domain extension of the independent component analysis (ICA) method and a joint diagonalization of the time varying spectra. Spectral and temporal-dependent features are then extracted from the separated sources using a new feature extraction method and subsequently used for target classification using a three-layer neural network. The performance of the developed algorithms are demonstrated on a subset of a real acoustic signature database acquired from the US Army TACOM-ARDEC, Picatinny Arsenal, NJ.

Citation Download Citation

M. R. Azimi-Sadjadi and S. Srinivasan "Blind separation of multiple vehicle signatures in frequency domain", Proc. SPIE 5796, Unattended Ground Sensor Technologies and Applications VII, (27 May 2005); https://doi.org/10.1117/12.610185

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available