25 April 2017 EASEE: an open architecture approach for modeling battlespace signal and sensor phenomenology
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Abstract
Open architecture in the context of defense applications encourages collaboration across government agencies and academia. This paper describes a success story in the implementation of an open architecture framework that fosters transparency and modularity in the context of Environmental Awareness for Sensor and Emitter Employment (EASEE), a complex physics-based software package for modeling the effects of terrain and atmospheric conditions on signal propagation and sensor performance. Among the highlighted features in this paper are: (1) a code refactorization to separate sensitive parts of EASEE, thus allowing collaborators the opportunity to view and interact with non-sensitive parts of the EASEE framework with the end goal of supporting collaborative innovation, (2) a data exchange and validation effort to enable the dynamic addition of signatures within EASEE thus supporting a modular notion that components can be easily added or removed to the software without requiring recompilation by developers, and (3) a flexible and extensible XML interface, which aids in decoupling graphical user interfaces from EASEE’s calculation engine, and thus encourages adaptability to many different defense applications. In addition to the outlined points above, this paper also addresses EASEE’s ability to interface with both proprietary systems such as ArcGIS. A specific use case regarding the implementation of an ArcGIS toolbar that leverages EASEE’s XML interface and enables users to set up an EASEE-compliant configuration for probability of detection or optimal sensor placement calculations in various modalities is discussed as well.
Conference Presentation
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Lauren E. Waldrop, D. Keith Wilson, Michael T. Ekegren, Christian T. Borden, "EASEE: an open architecture approach for modeling battlespace signal and sensor phenomenology", Proc. SPIE 10205, Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2017, 1020507 (25 April 2017); doi: 10.1117/12.2262658; https://doi.org/10.1117/12.2262658
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