With the development of new C4ISR paradigms like the Joint Battle-space Info-sphere (JBI), the High level Architecture (HLA) and the itegration of large simulations like JWARS-JMASS into a distributed synthetic battle-space the requirement to develop new and innovative mixed resolution modeling techniques is more critical than ever before. Analyzing C4ISR utility has historically been an issue because there has not been a clear paradigm like 'force on force attrition' is for a combat event. Rather the effects of C4ISR express themselves by modifying the force on force parameters. But by how much is an area of little consensus. We will discuss an approach, which uses the Dynamic Focusing Architecture (DFA) tool, recently developed for AFRL to help navigate these contentious waters.
Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. 12b. DISTRIBUTION CODE ABSTRACT (Maximum 200 Words)Modeling of real systems relies on the arduous task of describing the physical phenomena in terms of mathematical models, which often require excessive amounts of computation time when used in simulations. In the last few years there has been a growing acceptance of model abstraction whose emphasis rests on the development of more manageable models. Abstraction refers to the intelligent capture of the essence of the behavior of a model, without all the details. In the past, model abstraction techniques have been applied to complex models, such as Advanced Low Altitude Radar Model (ALARM) to simplify analysis. The scope of this effort is to apply model abstraction techniques to ALARM; a DoD prototype radar model for simulating the volume detection capability of low flying targets within a digitally simulated environment. Due to the complexity of these models, it is difficult to capture and assess the relationship between the model parameters and the performance of the simulation. Under this effort, ALARM parameters were modified and/or deleted and the impact on the simulation run time assessed. In addition, several meta-models were developed and used to assess the impact of ALARM parameters on the simulation run time. This report establishes a baseline for ALARM from which additional meta-models can be compared and analyzed.
Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. 12b. DISTRIBUTION CODE ABSTRACT (Maximum 200 Words)Modeling of real systems relies on the arduous task of describing the physical phenomena in terms of mathematical models, which often require excessive amounts of computation time when used in simulations. In the last few years there has been a growing acceptance of model abstraction whose emphasis rests on the development of more manageable models. Abstraction refers to the intelligent capture of the essence of the behavior of a model, without all the details. In the past, model abstraction techniques have been applied to complex models, such as Advanced Low Altitude Radar Model (ALARM) to simplify analysis. The scope of this effort is to apply model abstraction techniques to ALARM; a DoD prototype radar model for simulating the volume detection capability of low flying targets within a digitally simulated environment. Due to the complexity of these models, it is difficult to capture and assess the relationship between the model parameters and the performance of the simulation. Under this effort, ALARM parameters were modified and/or deleted and the impact on the simulation run time assessed. In addition, several meta-models were developed and used to assess the impact of ALARM parameters on the simulation run time. This report establishes a baseline for ALARM from which additional meta-models can be compared and analyzed.
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