Model-based Bayesian feature matching with application to synthetic aperture radar target recognition

Abstract We present a Bayesian approach for model-based classification from unordered, attributed feature sets. A set of features is estimated from measured data and is matched with a set predicted for each candidate hypothesis using a feature model. Both extracted and predicted feature sets have uncertainty, and some features may not be present in one set or the other. Computation of the match likelihoods requires a correspondence between estimated and predicted features, and two Bayesian correspondence methods are discussed. The proposed procedure is used to predict classification performance as a function of sensor parameters for a 10-vehicle target recognition problem using X-band synthetic aperture radar imagery.

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