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ATR performance modeling for building multiscenario adaptive systems

Modeling Automatic Target Recognition (ATR) system performance is important for a number of reasons. Many of these reasons have to do with the fact that performance models can enhance the ability to predict the ATR system performance in scenarios where data is not available. However, a critical use of ATR performance models that has not been explored until recently is the adaptation of the ATR system parameters. A system has been developed in recent years called Knowledge and Model-Based Algorithm Adaptation (KMBAA) for automatic ATR parameters adaptation. KMBAA has shown tremendous success in its ability to adapt ATR parameters and enhance the ATR system performance. KMBAA relies heavily on the use of complex ATR performance models. These models relate a number of ATR performance measures, such as probability of detection, to a number of ATR critical parameters, such as bright thresholds, and image/scene metrics, such as target range. The models being used in the KMBAA systems, and the process of building such models, are discussed in this paper.