Real world Operating Conditions (OCs) influence sensor data that in turn affects the performance of target detection and identification systems utilizing the collected information. The impact of operating conditions on collected data is widely accepted, but not fully characterized. OCs that affect data depend on sensor wavelength and associated scenario phenomenology, and can vary significantly between electro-optical (EO), infrared (IR), and radar sensors. This paper will discuss what operating conditions might be modeled for each sensor type and how they could affect automatic target recognition (ATR) systems designed to exploit their respective sensory data. The OCs are broken out into four categories; sensor, environment, target, and ATR algorithm training. These main categories will further contain subcategories with varying levels of influence. The purpose of this work is to develop an OC distribution model for the "real world" that can be used to realistically represent the performance of multiple ATR systems, and ultimately the decision made from the fused ATR results. An accurate OC model will greatly enhance the performance assessment of ATR and fusion systems by affording Bayesian conditioning in fusion performance analysis and aiding in the sensitivity analysis of fusion performance over different operational conditions. Accurate OC models will also be useful in the fusion algorithm operation.

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.