Current mission planning systems allow strike planners and operations centers to perform time-sensitive strike planning, execution monitoring, and validate mission effects using XML-based tools that visualize time critical attack plan and track plan status vs. execution. In this proposed STTR Phase I design for the Next Generation Navy Mission Planning (NGNMPS) system, we will identify expanded opportunities for the application of AI and ML algorithms/tools for intelligent, autonomous, and high-fidelity mission planning. The proposed AI approach can support NGNMPS by providing more accurate, less labor intensive, and increased fidelity strike planning. This proposed concept also takes a novel approach for the application of AI in the execution phase of the strike mission. By applying next generation ML processes for wireless communication systems, support for true digital interoperability and consequently enhanced performance across the Execution Mission Phase will be achieved. In addition to meeting the MLS and Cyber compliance requirements through the application of current methods and standards based procedures, the research aspect of this project will apply contemporary deep learning and other AI/ML algorithms to predict, identify, and counter the sophisticated attacks.
Benefit: The AI and ML approaches we propose for this STTR have their primary applications for the Next Generation Naval Mission Planning System (NGNMPS) to support airwing integration and interoperability across 4th and 5th generation platforms. As we mature this AI & ML, we envisage code developed in Phase III of this effort tailored for the next MAPEM deployment that will further mature the NGNMPS capability enabling multi-generational, cross platform access to planning information for Carrier Airwing, MAGTF, Joint Force and Coalition Operations. We also project numerous multi-use commercial applications where AI structured mission planning tools provide high fidelity pre-flight information that is further enhanced inflight with AI defined communication suites, balanced by contemporary deep learning and other AI/ML algorithms to predict, identify and counter against sophisticated information network attacks. We project that our proposed capabilities will assist in accelerating the introduction of unmanned commercial air cargo transportation into the civil airspace. Our commercialization strategy analysis also projects a significant use case for our proposed concept in the arena of emergency management/response at the Federal and State levels.
Keywords: cyber security, cyber security, mission planning, Reinforcement Learning, net enabled weapons, AI, ML, MLS
