KEYWORDS: Weapons, Data communications, Computer architecture, Computer simulations, Warfare, Intelligence systems, Performance modeling, Data modeling, Systems modeling, Defense and security
Traditionally, acquisition analyses require a hierarchical suite of simulation models to address engineering, engagement, mission and theater/campaign measures of performance, measures of effectiveness and measures of merit. Configuring and running this suite of simulations and transferring the appropriate data between each model are both time consuming and error prone. The ideal solution would be a single simulation with the requisite resolution and fidelity to perform all four levels of acquisition analysis. However, current computer hardware technologies cannot deliver the runtime performance necessary to support the resulting “extremely large” simulation. One viable alternative is to “integrate” the current hierarchical suite of simulation models using the DoD's High Level Architecture (HLA) in order to support multi-resolution modeling. An HLA integration -- called a federation -- eliminates the problem of “extremely large” models, provides a well-defined and manageable mixed resolution simulation and minimizes Verification, Validation, and Accreditation (VV&A) issues. This paper describes the process and results of integrating the Joint Modeling and Simulation System (JMASS) and the Joint Warfare System (JWARS) simulations -- two of the Department of Defense's (DoD) next-generation simulations -- using a HLA federation.
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 paper establishes a baseline for ALARM from which additional meta-models can be compared and analyzed.
KEYWORDS: C4ISR, Monte Carlo methods, Stochastic processes, Missiles, Defense and security, Data modeling, Analytical research, Computer architecture, Systems modeling, Weapons
With the development of new C41SR paradigms like the Joint Battle-space Info-sphere (JBI), the High Level Architecture (HLA) and the integration 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 ahs 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.
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 in their execution. 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, metamodels have been generated from complex models, such as the Tactical Electronic Reconnaissance Simulation Model (TERSM). The scope of this paper is to explore the ability of previously developed TERSM metamodels to accurately simulate the benchmark model using both limited subsets of the original data, and data subsets whose values are interpolated or extrapolated from the original data set used to generate and fit the model. This paper establishes a baseline from which additional metamodels can be compared and analyzed.
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