Paper
15 July 2002 Model abstraction and the simulation sandbox
Author Affiliations +
Abstract
The Air Force Hierarchy of Models, often referred to as the Great Pyramid, depicts the four disparate levels of resolution in which models are typically categorized. These levels range from an Engineering/Component level at the bottom, to Theater/Campaign level at the apex of the pyramid. Today, the landscape of simulations has evolved from uni-purpose, stove-piped simulations to those that provide a Joint Vision encompassing a much broader scope. Within the simulation community, there exists the desire for model reuse, particularly when it involves the reuse of validated legacy codes. Much effort has been put forth to integrate existing models into a federated system. Integrating models of similar resolution is difficult enough; yet, even more difficult is the more prevalent situation where models are represented at different levels of resolution. Often referred to as Mixed Resolution Modeling (or Multiresolution Modeling), it is arguably the most pressing problem facing the simulation research community today. This paper will describe an attempt to address the MRM problem by applying model abstraction techniques to reduce the complexity of a detailed model without sacrificing the essence of the model. This surrogate version of the detailed model will then be able to play within a more aggregate simulation environment. To demonstrate, JSAF (Joint Semi- Automated Forces) will be used to simulate the behavior of models at both the detailed and abstract levels. The results will be compared to demonstrate the impact and utility of model abstraction.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Dawn A. Trevisani, Alex F. Sisti, and Michael J. Mayhew "Model abstraction and the simulation sandbox", Proc. SPIE 4716, Enabling Technologies for Simulation Science VI, (15 July 2002); https://doi.org/10.1117/12.474915
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KEYWORDS
Radar

Visual process modeling

Statistical analysis

Target detection

Computer simulations

Statistical modeling

Systems modeling

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