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Proceedings Paper

Robust feature-based Bayesian ground target recognition using decision confidence for unknown target rejection
Author(s): John J. Westerkamp; Thomas Fister; Robert L. Williams; Richard A. Mitchell
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Paper 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.

Paper Details

Date Published: 13 August 1999
PDF: 12 pages
Proc. SPIE 3721, Algorithms for Synthetic Aperture Radar Imagery VI, (13 August 1999); doi: 10.1117/12.357655
Show Author Affiliations
John J. Westerkamp, Air Force Research Lab. (United States)
Thomas Fister, Veridian Inc. (United States)
Robert L. Williams, Air Force Research Lab. (United States)
Richard A. Mitchell, Qualia Computing, Inc. (United States)


Published in SPIE Proceedings Vol. 3721:
Algorithms for Synthetic Aperture Radar Imagery VI
Edmund G. Zelnio, Editor(s)

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