Implementing ML & AI for Automatic ELINT Identification

What AI-enabled suite of tools could enable the IC to increase the pace and quality of threat-processing and threat warning?  What are more robust ways to process data and decrease data-load on operators? From the most recent National Defense Strategy, there is a renewed focus on peer adversaries, along with the growing interest of incorporating machine learning techniques to aid operators in an increasingly clustered and contested electromagnetic environment. The dense electronic intelligence (ELINT) environment in these countries while performing strategic reconnaissance missions for the Air Force has highlighted the gaps in our automated equipment’s capacity to distinguish between land-based tracks and air-based tracks. While operators can eventually make the distinction between the two, the time necessary to conclude the difference between a Surface to Air Missile (SAM) or a Ship (surface track) vs an Airborne Interceptor (AI) would likely result in massive blue-force loss in a wartime scenario.