Evolutionary Algorithm-based Counterspace Operations Training System

The space segment forms a critical element of our global military dominance. With support for ISR, navigation, communications, and targeting for military operations, we have an ever-burgeoning reliance on our space systems. This reliance provides a potential opportunity to our adversaries to mitigate and attack our key space segment nodes. Additionally, the potential militarization of space by our adversaries only further exacerbates this vulnerability. To that end, the US Air Force is placing a heavy emphasis on maintaining space superiority by both refining doctrine and developing command and control and decision supports systems for protecting our space infrastructure. An associated emphasis must also be placed on the warfighter engaged in space operations, and specifically the associated training required by such counterspace operators. To address this opportunity in the Air Force counterspace military training domain, we propose to design and prototype an Evolutionary Algorithm-based Counterspace Operations Training System (EACOTS). EACOTS incorporates an evolutionary algorithm-based scenario generation and analysis component to stimulate appropriate training user responses and to achieve a real-time environment for enhanced OCS/DCS training. Additionally, the proposed system leverages our on-going work with AFRL/VS to develop counterspace decision support systems and with AFRL/HE to develop intuitive counterspace user interfaces.