Engagement and mission-level simulations utilize effects-based modeling, which increases computational speed, but may lead to inaccuracies, especially when evaluating the complex interactions between multiple radio frequency (RF) sources and targets. While the use of physics-based RF tools can increase the fidelity of the radar system analysis, the computational burden may become time consuming for realistic scenarios with multiple entities. As a result, their use is limited to only constructive environments and non-real time analysis. There is a need to develop a hybrid approach that leverages effects-based modeling for rapid analysis, but can also utilize validated physics-based models during key moments in the simulation. To address this need, the Advanced Framework for Simulation, Integration, and Modeling (AFSIM) was combined with a validated physics-based RF tool called Adaptive Sensor Prototyping ENvironinent (ASPEN™). AFSIM is a government off the shelf framework managed and developed by the Air Force Research Laboratory; this framework excels at managing different entities in a wide variety of missions and includes a robust visualization environment. ASPEN™ was developed by Georgia Tech Research Institute (GTRI) and models pulse-level RF effects that can then be processed using a variety of algorithms, including space-time adaptive processing. While linking these tools together would provide higher confidence in the RF-based results of a mission simulation, the computational burden could significantly increase. Therefore, it was necessary to develop an integration architecture that intelligently switches between effects-based and physics-based models when appropriate. This provides the benefit of ensuring that any scenario evaluation, whether for training, mission planning, or operational analysis, can be less computationally expensive while providing more confidence in the RF results. This paper describes the development of this integration architecture and demonstrates the capabilities of its implementation through a notional example.
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