Advanced Hierarchical Temporal Memory (HTM) and AI/ML Algorithm Study

An evolving set of missile threats will challenge Integrated Air and Missile Defense (IAMD) in the future. Advanced long-range radar is one component of a system to counter this threat, yet such powerful radars can also inadvertently detect and track a number of non-threat objects and signals in the environment. In Phase 1, TSC reviewed advanced methods in Artificial Intelligence (AI) and Machine Learning (ML), developed prototype AI/ML algorithms to mitigate the impact of such non-threat objects and signals on Ballistic Missile Defense (BMD) radars, and demonstrated the feasibility of this approach on a surrogate problem. In Phase II, TSC will expand the scope of work including exploring a broader set of AI/ML techniques, modifying the algorithms for application to specific operational systems, and validating the overall approach on measured and higher-fidelity simulated datasets. Approved for Public Release | 20-MDA-10643 (3 Dec 20)