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Commercial availability of very high-resolution synthetic aperture radar (SAR) imagery will enable development of automatic target recognition (ATR) algorithms to exploit its rich information content. This availability also permits exploration of both empirical and first principles approaches for predicting ATR performance. This paper describes a recent collection of high resolution SAR imagery. It details the operating conditions represented by the data and provides recommended experiments designed to challenge ATR algorithms and performance prediction. This set of information, along with the imagery, is contained in a Problem Set that will be made available to the community. The imagery is from a Deputy Under Secretary of Defense (DUSD) for Science and Technology (S&T) sponsored collection using the Sandia National Laboratory and General Atomics Lynx Sensor. The Lynx is now available as a commercial off-the-shelf (COTS) sensor. It was designed for use in medium-altitude UAVs and manned platforms. It operates at Ku-band frequency in stripmap, spotlight, and ground moving target indicator modes. Imagery in this collection was collected at 4' resolution and was then also reprocessed to 1' resolution. The collection included several military vehicles with significant variation in target, sensor, and background conditions. Defined experiments in the Problem Set present ATR algorithm development challenges by defining development (training) sets with limited representation of operating conditions and test sets that explore the algorithm's ability to extend to more complex operating conditions. These challenges are critical to military employment of ATR because the real world contains much more variability than it will be possible to explicitly address in an algorithm. For example, neither the storage nor the search through an exhaustive bay of templates is achievable for any realistic application. Thus, advanced developments that allow robust performance in denied conditions will accelerate the transition of ATR to the field. Additional experiments in the Problem Set present challenges in ATR performance prediction. Here, the development imagery provides empirical data to support development of prediction approaches. Test imagery provides an opportunity to validate the prediction technique's ability to, for example, interpolate or extrapolate performance.
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