Artificial Intelligence/Machine Learning (AI/ML) for Radio Frequency (RF) Modulation Recognition

DEPARTMENT OF THE ARMY SMALL BUSINESS INNOVATION RESEARCH (SBIR) PROGRAM SBIR 21.4 Broad Agency Announcement (BAA) Army Applied SBIR Opportunity (ASO) Announcement November 16, 2021: ASO issued for pre-release November 30, 2021: Army begins accepting proposals January 4, 2022: Deadline for receipt of proposals no later than 12:00 p.m. ET IMPORTANT Deadline for Receipt: Proposals must be completely submitted no later than 12:00 p.m. ET, January 4, 2022. Proposals submitted after 12:00 p.m. will not be evaluated. The final proposal submission includes successful completion of all firm level forms, all required volumes, and electronic corporate official certification. Classified proposals will not be accepted under the DoD SBIR Program. This BAA and the Defense SBIR/STTR Innovation Portal (DSIP) sites are designed to reduce the time and cost required to prepare a formal proposal. The DSIP is the official portal for DoD SBIR/STTR proposal submission. 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Proposers should refer to Section 4, Application and Submission information, of BAA 21.4 for detailed proposal preparation instructions. Proposals that do not comply with the requirements detailed in BAA 21.4 and the research objectives of this ASO are considered non-conforming and therefore are not evaluated nor considered for award. Phase II proposals shall not exceed 10 pages. Phase II commercialization strategy shall not exceed 10 slides. This should be the last section of the Technical Volume and will not count against the 10-page limit. Please refer to Appendix A of BAA 21.4 for detailed instructions on Phase II proposal preparation. c. Evaluation of Proposals Section 5, Evaluation of Proposals, in BAA 21.4 provides detailed information on proposal evaluation and the selection process for this ASO. d. Discretionary Technical and Business Assistance (TABA) Participation in the Army applied SBIR TABA program is voluntary for each Army Applied SBIR awardee. 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Army SBIR 21.4 Topic Index A214-049 Artificial Intelligence/Machine Learning (AI/ML) for Radio Frequency (RF) Modulation Recognition A214-049 TITLE: Artificial Intelligence/Machine Learning (AI/ML) for Radio Frequency (RF) Modulation Recognition OBJECTIVE: The purpose of this topic is to demonstrate the ability to interface to a modern Software Defined Radio (SDR) and the Photon digital signal processing framework in order to characterize large swaths of the RF spectrum in near-real-time (NRT) using AI/ML techniques for signal modulation recognition and sorting (Blue Force emitters; Red Force emitters; Civilian emitters); Demonstrate the ability to - learn new or unique threat signals of interest so they can be rapidly identified when they transmit. DESCRIPTION: The Electromagnetic Spectrum (EMS) is growing increasingly crowded on the battlefield with US and Coalition blue-force emissions, enemy red-force emissions, and emissions from civilian telecommunications infrastructure all present in the same electronic battlespace. US ground force tactical Signals Intelligence (SIGINT) and EW sensors require the ability to rapidly scan large swaths of the RF spectrum and automatically characterize emissions by frequency and modulation type using Machine Learning. Currently, modulation and signal recognition largely use libraries and look-up tables today. Process is very linear and is difficult to do in near-real-time with large amounts of data. The RCCTO sponsored a Mod Rec Challenge in FY19 with mixed results. If successful, Provide the ability to rapidly characterize the modulation type of signals transmitting within radio line of sight of tactical SIGINT/EW sensors on the battlefield. Provide the ability to learn a previously unknown threat signal of interest at the tactical edge so it can be identified and located when it transmits in the future. PHASE I: Phase I consists of completing Requirements Definition, developing digital Interfaces to SDR and Photon, and demonstrating initial Modulation Recognition AI/ML capabilities for selected signals. PHASE II: Phase II consists of demonstrating advanced Modulation Recognition capabilities for multiple signals, demonstrating ability to - Learn unique signal. Must - productize SW - Static and dynamic SW regression testing feature qualification testing, cybersecurity/IA scans, updates to system UI to implement SW, docs, etc. PHASE III: Phase III commercialization will provide a mature software for transition to Program of Record. KEYWORDS: Sensors; Automated intelligence; machine learning; modulation REFERENCES: Deep Learning for Modulation Recognition: A Survey With a Demonstration (https://ieeexplore.ieee.org/document/9058656) Modulation recognition using artificial neural networks (https://www.sciencedirect.com/science/article/abs/pii/S016516849600165X)