Fidelity score for ATR performance modeling

Automatic target recognition (ATR) performance modeling is dependent on model complexity, training data, and test analysis. In order to compare different ATR algorithms, we develop a fidelity score that characterizes the quality of different algorithms to meet real-world conditions. For instance, a higher fidelity ATR performance model (PM) is robust over many operating conditions (sensors, targets, environments). An ATR model that is run for one terrain, might not be applicable for all terrains, yet its operating manual clarifies its range of applicability. In this paper, we discuss a fidelity score that captures the performance application of ATR models and can be extended to different sensors over many operating conditions. The modeling quantification testing can be used as a fidelity score, validation metric, or guidance for model improvements. The goal is to provide a framework to instantiate a high fidelity model that captures theoretical, simulated, experimental, and real world data performance for use in a dynamic sensor manager.