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

摘要

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. !8