MDA: AI-Powered Battle Management for Integrated Air & Missile Defense (IAMD)
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Challenge
The integration of new Directed Energy (DE) weapons with traditional kinetic interceptors introduces a new layer of complexity to the battle management decision-making process for commanders in dynamic environments.
Our Solution
We developed a proof-of-concept AI/ML-powered Situational Awareness and Fire Control Tool (SAFCT). This tool uses machine learning models, trained on data generated from complex AFSIM scenarios, to predict the Probability of Kill (Pk) for various weapon-target pairings in near real-time. An integrated assignment optimizer then recommends the most effective weapon assignments.
Impact
We successfully demonstrated a prototype tool that provides commanders with a data-driven decision aid for managing complex IAMD scenarios involving both kinetic and non-kinetic weapon systems.
More Case Studies
NAVSEA / PEO-IWS: M&S Optimization for AEGIS Modernization
To counter rapidly evolving anti-ship cruise missile threats, the AEGIS Combat System (ACS) requires frequent software updates. Identifying the optimal software-only changes from a nearly unbounded set of configurable parameters is a significant challenge for analysts.
Continuous Test & Evaluation for AI-Powered Fire Control
When developing an AI agent for a critical system, like an Integrated Air and Missile Defense (IAMD) fire control, developers must continuously verify that the model behaves correctly and that new updates do not introduce regressions. Manually testing the AI’s performance across all possible engagement scenarios is impossible.