Containerized RF Application Automated Mission Manager (CRAAMM) Phase II

Microservices-Based (MB) Radio Frequency (RF) and Electromagnetic Spectrum Operations (EMSO) systems are the future for the US DoD as evidenced by the numerous MB-EMSO payload development and maturation initiatives across the DAF, Navy, and Army. Further, many defense contractors are developing their own MB-EMSO systems outside the large DAF, Army, and Navy efforts, several of which are aligning with the Sensor Open Systems Architecture (SOSA) to improve transition and sustainment success. While these MB-EMSO systems are powerful and flexible, operators must manually configure and reconfigure the systems, which places a tremendous cognitive burden on the operators and their ability to rapidly draw upon their expertise and field knowledge. These MB-EMSO systems are critically dependent upon an operator monitoring and running the systems as well as starting, stopping, and employing software applications, thereby restricting employment to situations where an operator is available full-time. MB-EMSO systems are intended to run semi or fully autonomously, so that warfighters can focus on the most pressing matters, yet to date, MB-EMSO systems all require manual operation. Statheros’ Containerized RF Application Automated Mission Manager (CRAAMM) is a container orchestrator, service mesh, and deployment manager for microservices-based (MB) radio frequency (RF) and Electromagnetic Spectrum Operations (EMSO) systems. CRAAMM enables autonomous and rapid reconfiguration and deployment of containerized RF applications based on dynamic environmental events, such as the detection of emergent RF threats, movement through geo-bounded areas, changes in signals of interest (SOIs) or RF threat behaviors, or other unanticipated events in the electromagnetic operating environment (EMOE). CRAAMM is specifically designed to reduce or eliminate the cognitive burden on RF and EMSO system operators by autonomously managing and optimizing system resources to maximize system efficacy based on dynamic, real-time EMOE events and activities.  Under Phase I, Statheros proved the feasibility of CRAAMM by developing a concept demonstrator and then demonstrating the capability in a virtualized sandbox environment for a DAF EMSO mission system. Under this Phase II effort, Statheros will mature and enhance the Phase I CRAAMM concept demonstrator by expanding artificial intelligence algorithms, developing support for new EMSO mission systems, enhancing inter-application message support, and integrating and testing CRAAMM with two DAF EMSO mission systems to prepare CRAAMM for transition to Phase III follow-on work with multiple DAF stakeholders.