Laser vibrometry for target classification

Mark R. Stevens; Magnus Snorrason; Daniel J. Petrovich

doi:10.1117/12.477048

25 July 2002 Laser vibrometry for target classification

Mark R. Stevens, Magnus Snorrason, Daniel J. Petrovich

Proceedings Volume 4726, Automatic Target Recognition XII; (2002) https://doi.org/10.1117/12.477048
Event: AeroSense 2002, 2002, Orlando, FL, United States

Abstract

Automatic Target Recognition (ATR) algorithm performance is sensitive to variability in the observed target signature. Algorithms are developed and tested under a specific set of operating conditions and then are often required to perform well under very different conditions (referred to as Extended Operating Conditions, or EOCs). The stability of the target signature as the operating conditions change dictates the success or failure of the recognition algorithm. Laser vibrometry is a promising sensor modality for vehicle identification because target signatures tend to remain stable under a variety of EOCs. A micro-doppler vibrometry sensor measures surface deflection at a very high frequency, thus enabling the surface vibrations of a vehicle to be sensed from afar. Vehicle identification is possible since most vehicles with running engines have a unique vibration signature defined by the engine type. In this paper, we present an ATR algorithm that operates over data collected from a set of accelerometers. These contact accelerometers were placed at a variety of locations on three target vehicles to emulate an ideal laser vibrometer. We discuss a set of features that are useful for discrimination of the three different target categories. We also present classification results based on these features.

Citation Download Citation

Mark R. Stevens, Magnus Snorrason, and Daniel J. Petrovich "Laser vibrometry for target classification", Proc. SPIE 4726, Automatic Target Recognition XII, (25 July 2002); https://doi.org/10.1117/12.477048

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