Cultivating PWSA Through Digital Life-Cycle Integration, Test, and Evaluation

To realize the rapid pace desired by the Space Development Agency (SDA) and to enhance decisions and communication across missions, systems, and acquisitions lifecycles, we propose a phased approach to Test & Evaluation (T&E). This will help SDA overcome some of the early adoption and implementing challenges of Digital Engineering (DE) transformation. Specifically, we propose to position SDA to leverage the benefits of this new paradigm in application to the development of the Proliferated Warfighter Space Architecture (PWSA). We plan to do this by adopting the phased approach using model-based systems engineering (MBSE) methods supported by Modeling, Simulation, and Analysis (MSA) tools. This involves the use of transformative tools, processes, and methods through phases: Model-Based T&E Planning and Control. Dynamic T&E Planning and Execution. and Coupled Mission, System, and T&E Decision Making. In application to Phase I of the proposed work, this includes harmonization of sophisticated data and models being developed by distinct groups, in diverse formats and for differing purposes to implement the PWSA. To perform such harmonization, we propose to exploit and refine the framework we have been developing to conjoin modeling and simulation (M&S) and MBSE. Based on this framework, we seek to develop new processes fitting the SDA needs to overcome the burden of its current VV&A processes. This will entail enhancing the PWSA to enable the MBSE/M&S framework’s homomorphism concepts to accumulate confidence in the validity of the models. In Phase I we propose to exploit and refine our framework to conjoin M&S and MBSE in Task 1. Based on this framework, Task 2 will develop new processes fitting the SDA needs to overcome the burden of its current VV&A processes. This will be demonstrated in Task 3 in application to an example case study. Upon successful completion of the project, we will show how to apply the approach to the PWSA so as most effectively and efficiently accumulate confidence in the validity of the models and data as they mutually inform each other over time.