Validation of SAR ATR performance prediction using learned distortion models

Michael Boshra; Bir Bhanu

doi:10.1117/12.396366

24 August 2000 Validation of SAR ATR performance prediction using learned distortion models

Michael Boshra, Bir Bhanu

Proceedings Volume 4053, Algorithms for Synthetic Aperture Radar Imagery VII; (2000) https://doi.org/10.1117/12.396366
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

Performance prediction of SAR ATR has been a challenging problem. In our previous work, we developed a statistical framework for predicting bounds on fundamental performance of vote-based SAR ATR using scattering centers. This framework considered data distortion factors such as uncertainty, occlusion and clutter, in addition to model similarity. In this paper, we present an initial study on learning the statistical distributions of these factors. We focus on the development of a method for learning the distribution of a parameter that encodes the combined effect of the occlusion and similarity factors on performance. The impact of incorporating such a distribution on the accuracy of the predicted bounds is demonstrated by comparing bounds obtained using it with those obtained assuming simplified distributions. The data used in the experiments are obtained from the MSTAR public domain under different configurations and depression angles.

Citation Download Citation

Michael Boshra and Bir Bhanu "Validation of SAR ATR performance prediction using learned distortion models", Proc. SPIE 4053, Algorithms for Synthetic Aperture Radar Imagery VII, (24 August 2000); https://doi.org/10.1117/12.396366

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