Performance modeling for automatic target recognition systems

Anne Catlin; Lemuel R. Myers; Kenneth W. Bauer; Steven K. Rogers; Randy P. Broussard

doi:10.1117/12.281555

28 July 1997 Performance modeling for automatic target recognition systems

Anne Catlin, Lemuel R. Myers, Kenneth W. Bauer, Steven K. Rogers, Randy P. Broussard

Proceedings Volume 3070, Algorithms for Synthetic Aperture Radar Imagery IV; (1997) https://doi.org/10.1117/12.281555
Event: AeroSense '97, 1997, Orlando, FL, United States

Abstract

This paper explores using linear regression and artificial neural networks (ANN) to model the performance of an ATR algorithm based on a given set of data. Here, a probability of detection response surfaces as a function of relevant parameters is simulated. It is then shown that this surface can be approximated using either linear regression or an ANN with good results. These regression surfaces can provide valuable information to the ATR developer/customer in terms of trying to predict ATR performance in untested areas. The application of this ATR performance modeling methodology becomes clear when we consider applying it to a common problem, such as air-to-ground target detection, where the changing parameters of the target can give a good set of data points from which to build the response curve.

Citation Download Citation

Anne Catlin, Lemuel R. Myers, Kenneth W. Bauer, Steven K. Rogers, and Randy P. Broussard "Performance modeling for automatic target recognition systems", Proc. SPIE 3070, Algorithms for Synthetic Aperture Radar Imagery IV, (28 July 1997); https://doi.org/10.1117/12.281555

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