Research Associates of Syracuse Inc. — Department of Defense SBIR Phase I: AF193-025

Description

This effort addresses a need in EW performance testing for high fidelity modeling and simulation (M&S), and signal environment generation for multiple same-type, multi-agile radar emitters. It has broad applicability to algorithm development, cognitive (COG) systems, and machine learning (ML) applications. To properly evaluate multi-agile emitters, it is essential that signal waveform characteristics such as modulations (intentional or unintentional), and classic parameter agilities and intra-pulse timing are properly represented. The RAMGET software data tool enables the rapid generation and synthesis of sufficient and necessary data quantities; crucial for ML applications, where it is well-known by the community that large amounts of representative data are required for training \ test. Statistical methods synthesize multiple, representative modulations, including unique emitter discriminants, based on a limited set of real-world emitter data. RAS expertise in emitter collection, analysis, M&S, and generation of waveforms and scenarios is leveraged. In Phase I the product-market fit is validated, feasibility study completed addressing potential users, benefits, objectives, and key measurable results. The latter is demonstrated using the prototype developed in Phase II. In Phase III, transitions to AF and other DoD users are pursued, including laboratory, field and virtual test ranges and to contractors for realistic performance evaluation.