MSTAR's extensible search engine and model-based inferencing toolkit

John Wissinger; Robert Ristroph; Joseph R. Diemunsch; William E. Severson; Eric Fruedenthal

doi:10.1117/12.357671

13 August 1999 MSTAR's extensible search engine and model-based inferencing toolkit

John Wissinger, Robert Ristroph, Joseph R. Diemunsch, William E. Severson, Eric Fruedenthal

Proceedings Volume 3721, Algorithms for Synthetic Aperture Radar Imagery VI; (1999) https://doi.org/10.1117/12.357671
Event: AeroSense '99, 1999, Orlando, FL, United States

Abstract

The DARPA/AFRL 'Moving and Stationary Target Acquisition and Recognition' (MSTAR) program is developing a model-based vision approach to Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR). The motivation for this work is to develop a high performance ATR capability that can identify ground targets in highly unconstrained imaging scenarios that include variable image acquisition geometry, arbitrary target pose and configuration state, differences in target deployment situation, and strong intra-class variations. The MSTAR approach utilizes radar scattering models in an on-line hypothesize-and-test operation that compares predicted target signature statistics with features extracted from image data in an attempt to determine a 'best fit' explanation of the observed image. Central to this processing paradigm is the Search algorithm, which provides intelligent control in selecting features to measure and hypotheses to test, as well as in making the decision about when to stop processing and report a specific target type or clutter. Intelligent management of computation performed by the Search module is a key enabler to scaling the model-based approach to the large hypothesis spaces typical of realistic ATR problems. In this paper, we describe the present state of design and implementation of the MSTAR Search engine, as it has matured over the last three years of the MSTAR program. The evolution has been driven by a continually expanding problem domain that now includes 30 target types, viewed under arbitrary squint/depression, with articulations, reconfigurations, revetments, variable background, and up to 30% blocking occlusion. We believe that the research directions that have been inspired by MSTAR's challenging problem domain are leading to broadly applicable search methodologies that are relevant to computer vision systems in many areas.

Citation Download Citation

John Wissinger, Robert Ristroph, Joseph R. Diemunsch, William E. Severson, and Eric Fruedenthal "MSTAR's extensible search engine and model-based inferencing toolkit", Proc. SPIE 3721, Algorithms for Synthetic Aperture Radar Imagery VI, (13 August 1999); https://doi.org/10.1117/12.357671

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