CREATING SPECIAL OPERATIONS FORCES' ORGANIC SMALL UNMANNED AIRCRAFT SYSTEM OF THE FUTURE

Emerging observations of the Ukrainian conflict reinforce standing assumptions that the future’s multidomain operational environment in which U.S. special operations forces (SOF) will deploy will be characterized by rapid, continuous advancements in intelligence, surveillance, and reconnaissance (ISR) technologies. The Department of Defense’s inability to develop and field innovative weapons and technical systems at the rate of its peer and near-peer competitors invites great risks to both its military members and its national security. One critical deficiency that U.S. SOF must address immediately is its use of artificial intelligence and machine learning in a small unmanned aircraft system (sUAS). We can no longer assume that we will achieve the air superiority or air parity that have enabled the persistent presence of theater-level assets to support military elements in contested and denied areas. This thesis focuses on enhancing U.S. SOF force protection and situational awareness by combining a cutting-edge sUAS with object recognition software to create an organic ISR capability. Following several field experiments in partnership with private industries to test and refine object recognition software when combined with a sUAS, our results strongly suggest that integrating object recognition capabilities into a SOF element’s organic sUAS can achieve the performance parameters necessary to fill the current gap in U.S. SOF force protection and ISR requirements.SOCOM S&TOffice of Naval ResearchMajor, United States ArmyApproved for public release. Distribution is unlimited