RT&L FOCUS AREA(S): Artificial Intelligence (AI)/Machine Learning (ML);General Warfighting Requirements (GWR) TECHNOLOGY AREA(S): Air Platforms;Electronics The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with section 3.5 of the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws. OBJECTIVE: Design and develop advanced low probability of intercept/low probability of detection (LPI/LPD) radar techniques using artificial intelligence (AI) driven methods. DESCRIPTION: The use of low probability of intercept/low probability of detection (LPI/LPD) radar techniques in radar and communication systems operating in adversarial environments has been common for many years. A wide range of techniques have been utilized in various combinations, including wide operational bandwidth, frequency agility, proper power management, antenna side lobe reduction, and advanced scan patterns, as well as host LPI waveform designs including binary phase codes, polyphase codes, Barker codes, Frank codes, and Polytime codes. Countermeasures to these techniques have been widely documented in open source literature. Many of the more recent approaches rely on machine- and deep learning-based detection and localization algorithms to dramatically reduce the effectiveness of conventional LPI/LPD radar techniques. The Navy requires the development of highly adaptive, advanced AI-based LPI/LPD radar techniques to regain and maintain an enduring advantage in the presence of capable adversaries. Computational resources to host this capability vary significantly across the candidate transition platforms. As a result, the computational efficiency of the approach along with its robustness and training requirements should be considered important criteria in the selection of an AI technique. Work produced in Phase II may become classified. Note: The prospective contractor(s) must be U.S. owned and operated with no foreign influence as defined by DoD 5220.22-M, National Industrial Security Program Operating Manual, unless acceptable mitigating procedures can and have been implemented and approved by the Defense Counterintelligence and Security Agency (DCSA) formerly Defense Security Service (DSS). The selected contractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances. This will allow contractor personnel to perform on advanced phases of this project as set forth by DCSA and NAVAIR in order to gain access to classified information pertaining to the national defense of the United States and its allies; this will be an inherent requirement. The selected company will be required to safeguard classified material IAW DoD 5220.22-M during the advanced phases of this contract. PHASE I: Develop concepts for multiple synergistic LPI/LPD radar techniques that are able to quickly adapt in response to the tactical environment. One or more AI-based decisions engines to achieve this adaptability. The concepts should directly address how the approach will counter those advanced machine and deep learning LPI/LPD radar techniques widely discussed in open source literature. Consideration should be given to how invasive each conceptual approach is in terms of hardware requirements and performance impacts. The Phase I effort will include prototype plans to be developed under Phase II. PHASE II: Based on Phase I results, candidate concept(s) will be matured through more detailed high-fidelity analyses with a focus on a particular legacy radar system. Examine sensor integration concepts. Working with the Navy sponsor, assess hardware, software, and firmware impacts to accommodate the candidate techniques. Identify critical technical challenges and perform necessary analysis and as required experimentation to understand the associated risk. The Phase II deliverable should provide a detailed conceptual approach with supporting analyses of sufficient detail to support follow-on design and integration in the candidate radar system. Work in Phase II may become classified. Please see note in Description section. PHASE III DUAL USE APPLICATIONS: Complete development, perform final testing, integrate, and transition the final solution to naval airborne radar systems. The techniques could be utilized by commercial applications in commercial communication and data systems. REFERENCES: 1. Kong, S. H., Kim, M., Hoang, L. M. and Kim, E. “Automatic LPI radar waveform recognition using CNN.” IEEE Access, 6, 2018, pp. 4207-4219. https://doi.org/10.1109/ACCESS.2017.2788942. 2. Kookamari, F. H., Norouzi, Y. and Nayebi, M. M. “Using a moving aerial platform to detect and localise a low probability of intercept radar.” IET Radar, Sonar & Navigation, 11(7), 2017, pp. 1062-1069. https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2016.0295. 3. Alrubeaan, T., Albagami, K., Ragheb, A., Aldosari, S., Altamimi, M. and Alshebeili, S. “An Investigation of LPI Radar Waveforms Classification in RoF Channels.” IEEE Access, 7, 2019, pp. 124844-124853. https://doi.org/10.1109/ACCESS.2019.2938317.