Sensor system performance models (i.e., ATR performance models (APMs)) are needed online for decision making, multi-source fusion, sensor planning and parameter setting and offline to understand performance in operational settings for ATR development, down selects, and transition decisions and mission level simulations generally. Modern sensor systems involve resolved targets and non-human / ATR exploitation. This brings special modeling challenges, e.g., ATRs' additional sensitivities and data set mismatches. APM development is a multi-disciplinary problem. Perspectives from these disciplines will introduce both APM challenges and challenge surmounting approaches via the COMPASE Model Framework (CMF). From the confusion matrix perspective, the challenge of operating condition (OC) space resolution, which is needed for APM sensitivity and for broad model applicability, is introduced along with CMF's OC tree approach. From the sensor modeling perspective, the sensor system modeling has the challenge of taking full advantage of the rich first principles understanding of sensor effects while including effects beyond sensor image metrics. This challenge is approached in CMF through pixels-on-target adjustments based on uniqueness, clarity and conformity. From the machine learning perspective, there is the challenge of taking advantage of empirical evidence for ATR performance, even though it is non-representative in terms of OC distribution. This is being approached in CMF through stratification of OC space and explicit accounting of OC distributions. The CMF approach is realized in an APM development and assessment framework and has resulted in a suite of APMs that are being used in online and offline applications.

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Timothy D. Ross, Jeffrey P. Duffy, Richard J. Thomas, Wesley A. Jones, Jordan Garcia, Hyatt B. Baker, and Andrew C. Rice, "Modeling the performance of modern sensor systems," Proc. SPIE 10988, Automatic Target Recognition XXIX, 109880Z (Presented at SPIE Defense + Commercial Sensing: April 17, 2019; Published: 14 May 2019); https://doi.org/10.1117/12.2518887.