A theoretical approach to C4ISR architectures

Within Army transformation towards the future force concept, C4ISR architectures play the central role in the evolution of the systems and the doctrine. The scope and size of C4ISR has increased significantly over the past few years, and material developers are trying to evolve their C4ISR architecture concepts and development methods to be able to satisfy these trends. In the process, they are discovering that many of the methods and processes used in the past are not adequate to handle the increased scope and size, as well as the compressed development timelines. The C4ISR architecture framework is at the center of all developments, that provides the templates (called "views") of how to capture C4ISR system design information, and it's intended operational use. While this framework is still valid, the current use of the framework lacks the rigor and consistency of a theoretical systematic approach of how to generate and interrelate the underlying design data. In this paper we present a rigorous mathematical framework augmenting the existing architecture framework, to provide a systematic way of capturing and generating the C4ISR system design data and translating it in standard architecture framework views. The approach starts with defining a metamodel of the design, which is used to capture the System of Systems (SoS) design parameters, the sub-system design parameters, and their relationships with the operating requirements. We then define the C2, Communications and Sensor requirements/capabilities of the system, and through a rigorous mathematical framework of relating this data with operational data about the intended use, we create a set of analytical expressions that generate data which when put in the appropriate format form the basis of the C4ISR architecture views. This way we have full traceability between the system design and the architecture framework views. This approach is rigorous, systematic, and can generate all the data and the architecture views in much shorter timelines and greater consistency and accuracy, while providing a much more robust basis for the design.