Machine-Assisted Electronic Protection

OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Trusted AI and Autonomy 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 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: The objective of this Small Business Innovation Research (SBIR) Direct to Phase II topic is to develop an innovative system for Opportunistic Selection and Waveform Adaptation (OSWA) to resist adversary jamming. OSWA will use cognitive radar utilizing machine learning to intelligently select and adjust resistant waveforms during real-world operation response. DESCRIPTION: Cognitive radar has shown some capability to automate intelligent radar responses to the electronic environment. This includes being able to quickly identify and react to electronic attacks such as jamming or deception. Phase I developed the OSWA to make this technology feasible. Therefore, this SBIR phase II prototype shall be able but not limited to improve cognitive intelligence in radar functionality. (i.e., it will develop Opportunistic Selection and Waveform Adaptation for electronic warfare (EW).) Phase II will also seek to increase signal processing and EW hardware systems for radar technology. This phase II seeks efficient prototype development that will be operationally used in the Air Force. PHASE I: As this is a Direct-to-Phase-II (D2P2) topic, no Phase I awards will be made as a result of this topic. To qualify for this D2P2 topic, the Government expects the applicant(s) to demonstrate feasibility by means of a prior Phase I-type effort that does not constitute work undertaken as part of a prior or ongoing SBIR/STTR funding agreement. This topic aims to develop an advanced Opportunistic Selection and Waveform Adaptation (OSWA) system, leveraging AI and ML, particularly in cognitive electronic warfare. The OSWA system will enhance modern radar systems' resilience against electronic attacks like barrage noise and deceptive jamming by dynamically selecting and adjusting jam-resistant waveforms using real-time feedback on effectiveness and adversary behavior. Phase I focused on demonstrating continual adaptation of waveforms to counter electronic attacks, combining expert rules and reinforcement learning, integrating cutting-edge machine learning algorithms with signal processing and electronic warfare hardware proficiency. This technology is currently at an acceptable stage to award a D2P2. PHASE II: For this phase II, the proposals should include development, installation, integration, demonstration, test and evaluation of the proposed solution prototype system enhancement of their Phase I like prototype equivalent solution. This Phase II will seek to implement and demonstrate a realistic case scenario of cognitive EW successfully resisting adversary jamming with radar. Moreover, this SBIR phase II prototype shall be able but not limited to successfully resist adversary jamming through cognitive radar. PHASE III DUAL USE APPLICATIONS: Phase III efforts will focus on transitioning operationally ready technology to a commercial sector or DoD environment. The offeror will identify transition partners. TRL should be at a minimum of a TRL 6. The AI technology will have a well-developed radar functionality to deliver the realization of such technology to the war fighter or commercial sector. Once this technology hits the required rightness level, then it will be integrated in the ARTS V3 ground radar program. REFERENCES: https://www.sbir.gov/node/2328925; https://ieeexplore.ieee.org/document/9035657; https://ieeexplore.ieee.org/document/9446140; KEYWORDS: machine learning; artificial intelligence; data fusion; space domain awareness; cognitive ew; cognitive radar;