Assumption truth maintenance in model-based ATR algorithm design

Laura Fulton Bennett; Rubin Johnson; Cecil Ivan Hudson

doi:10.1117/12.44857

1 August 1991 Assumption truth maintenance in model-based ATR algorithm design

Laura Fulton Bennett, Rubin Johnson, Cecil Ivan Hudson

Proceedings Volume 1470, Data Structures and Target Classification; (1991) https://doi.org/10.1117/12.44857
Event: Orlando '91, 1991, Orlando, FL, United States

Abstract

In a given approach to automatic target recognition (ATR) algorithm design, an underlying network of assumptions provides computational and conceptual efficiency. This network includes concrete assumptions about the physical characteristics of the real-world scene and abstract assumptions about knowledge acquisition and representation. A facility for the identification and tracking of assumptions in dynamic systems is critical for algorithm design and performance evaluation purposes. The intersection of assumptions at a designated stage of the target recognition process defines the valid domain of application of the ATR system. An approach to assumption truth maintenance for application to complex, visual pattern recognition systems is described. The types of assumptions made in key-feature, model-based ATR systems are systematically identified, from the low-level pixel domain to the high-level mission statement. The approach permits the tracking of algorithm assumptions as they propagate through the pattern recognition process and provides for belief formation and revision to maintain consistency. The approach is demonstrated on a prototype set of infrared test imagery at varying levels of resolution and signal-to-noise ratio, representative of the given problem domain.

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Laura Fulton Bennett, Rubin Johnson, and Cecil Ivan Hudson "Assumption truth maintenance in model-based ATR algorithm design", Proc. SPIE 1470, Data Structures and Target Classification, (1 August 1991); https://doi.org/10.1117/12.44857

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KEYWORDS

Automatic target recognition

Detection and tracking algorithms

Systems modeling

Evolutionary algorithms

Model-based design

Algorithm development

Data modeling

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