There is a strong and growing need for automatic target recognition (ATR) technologies. Those technologies have made
great strides; however, there is a general sense that they are not having the full impact desired. This paper develops a
value-based framework for considering how ATR technology can be made more relevant and then introduces and
expands on two elements within that framework: 'enhancements' and 'accommodations'. Value is used here as the
degree to which a technology's benefits exceed the technology's costs. Value may be improved by increasing benefits or
decreasing costs; but it may be as important that the uncertainty about benefits and costs be reduced. Enhancements and
accommodations are distinguished here from the 'core ATR'. While it is generally appreciated that improved core ATR
performance could improve value, enhancements and accommodations might be overlooked by those focused on ATRs.
Enhancements are ways of making the overall system, inclusive of a core ATR, more capable. Accommodations are
ways of making the problem easier for the core ATR. An example enhancement is technology to fuse the output of the
core ATR with other sources. An example accommodation is for the user to agree to limit the target set to large, and
therefore more easily recognized, objects. This paper encourages the consideration of this framework and outlines a
number of candidates for enhancements and accommodations for synthetic aperture radar (SAR) ATR, including
humans-in-the-loop, change detection, fusion, modeling confusers, group detection, adaptive algorithms, class make-up,
and scene-based decisions.