Bounding SAR ATR performance based on model similarity

Similarity between model targets plays a fundamental role in determining the performance of target recognition. We analyze the effect of model similarity on the performance of a vote-based approach for target recognition from SAR images. In such an approach, each model target is represented by a set of SAR views sampled at a variety of azimuth angles and a specific depression angle. Both model and data views are represented by locations of scattering centers, which are peak features. The model hypothesis (view of a specific target and associated location) corresponding to a given data view is chosen to be the one with the highest number of data-supported model features (votes). We address three issues in this paper. Firstly, we present a quantitative measure of the similarity between a pair of model views. Such a measure depends on the degree of structural overlap between the two views, and the amount of uncertainty. Secondly, we describe a similarity-based framework for predicting an upper bound on recognition performance in the presence of uncertainty, occlusion and clutter. Thirdly, we validate the proposed framework using MSTAR public data, which are obtained under different depression angles, configurations, and articulations.