Robust feature-based Bayesian ground target recognition using decision confidence for unknown target rejection

John J. Westerkamp; Thomas Fister; Robert L. Williams; Richard A. Mitchell

doi:10.1117/12.357655

13 August 1999 Robust feature-based Bayesian ground target recognition using decision confidence for unknown target rejection

John J. Westerkamp, Thomas Fister, Robert L. Williams, Richard A. Mitchell

Author Affiliations +

Proceedings Volume 3721, Algorithms for Synthetic Aperture Radar Imagery VI; (1999) https://doi.org/10.1117/12.357655
Event: AeroSense '99, 1999, Orlando, FL, United States

Abstract

The statistical feature-based (StaF) classifier is presented for robust high range resolution (HRR) radar moving ground target identification. The target features used for classification are the amplitude and location of HRR signature peaks. The peak features are not predetermined using the training data but are extracted on-the-fly from the observed HRR profile and are different for each target observation. A classifier decision is made after statistical evidence is accrued from each feature and across multiple looks. Decision uncertainty is estimated using a belief-based confidence measure. Classifier decisions are rejected if the decision uncertainty is too high since it is likely that the observed HRR profile is not in the classifier's target database. Robustness is achieved by using only peak features rather than the entire HRR profile (much of which is low-level scatterers buried in noise or simply noise) and by rejecting decisions with high uncertainty.

Citation Download Citation

John J. Westerkamp, Thomas Fister, Robert L. Williams, and Richard A. Mitchell "Robust feature-based Bayesian ground target recognition using decision confidence for unknown target rejection", Proc. SPIE 3721, Algorithms for Synthetic Aperture Radar Imagery VI, (13 August 1999); https://doi.org/10.1117/12.357655

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