SBIR-STTR Award

Technology Service Corporation (TSC) proposes to identify Integrated Air and Missile Defense (IAMD) scenarios of interest, and assess a wide variety of advanced state-of-the-art Machine Learning (ML) and Artificial Intelligence (AI) technologies for post-intercept and other forms of debris, clutter, and electronic attack/electronic protection (EA/EP) applications in missile defense radars. The study will further consider baseline and advanced radio frequency (RF) signal processing techniques. Approved for Public Release | 19-MDA-10270 (18 Nov 19)

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)