ATR performance modeling for building multiscenario adaptive systems

Hatem N. Nasr

doi:10.1117/12.45735

1 July 1991 ATR performance modeling for building multiscenario adaptive systems

Hatem N. Nasr

Proceedings Volume 1483, Signal and Image Processing Systems Performance Evaluation, Simulation, and Modeling; (1991) https://doi.org/10.1117/12.45735
Event: Orlando '91, 1991, Orlando, FL, United States

Abstract

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.

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Hatem N. Nasr "ATR performance modeling for building multiscenario adaptive systems", Proc. SPIE 1483, Signal and Image Processing Systems Performance Evaluation, Simulation, and Modeling, (1 July 1991); https://doi.org/10.1117/12.45735

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KEYWORDS

Performance modeling

Automatic target recognition

Systems modeling

Image segmentation

Data modeling

Image processing

Optimization (mathematics)

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