AI enabled Advanced Battle Planning & Management (AIM)

Field commanders are facing an increasing array of space and aerial missile threats from adversaries of the United States. The threats range from ballistic missiles (BM) of various ranges and payload capabilities, cruise missiles (CM), maneuverable reentry vehicle (MaRV), hypersonic glide vehicles (HGVs), hypersonic cruise missiles (HCMs), and fractional orbital bombardment systems (FOB). The adversaries are developing capabilities that attempt to exploit the gaps in the sensor coverage and engagement capabilities of the currently deployed defense systems of the US. In order to stay ahead of the threats, the US defense system needs to augment and transform sensor systems, command-control-and-communication (C3), effectors and interceptors, and Artificial Intelligence/Machine Learning (AI/ML) and automation technologies. The proposed Small Business Innovation Research (SBIR) project will focus attention on the AI/ML and automation technologies for transforming the missile defense battle management that can exploit the developments in the other three systems (sensors, C3, kinetic and non-kinetic effectors). The proposed AI enabled Advanced Battle Planning & Management (AIM) will revolutionize decision making speed and capacity of field commanders during real-time battle management scenarios, where seconds count and the stakes of decision errors are very high and the situational awareness and environmental conditions are changing continually. AIM methods and technologies will also aid other stake-holders such as strategic commanders and training and war-gaming personnel to anticipate and prepare for various threat scenarios with the aid of AI/ML threat generation capabilities. At the crux of the proposed approach is the AIM learning framework, which is a modular and extendable software AI/ML architecture comprising of AI/ML algorithms and models to perform rapid and optimal decision making for (a) obtaining comprehensive situational awareness, and (b) fire-control and engagement planning for prosecution of the threat. Approved for Public Release | 24-MDA-11906 (16 Sep 24)