DAF27TZ02-DV005 — RF Spectrum Operations Enhanced with Distributed AI-Assisted Sharing of Knowledge Sharing (RF-DASK)

Develop a dynamic and scalable signal representation to describe complex waveforms in a compressed format for RF spectrum knowledge sharing between Electronic Support (ES) systems and control the deployment and operation of Electronic Countermeasures. The proposed solution should support distributed operation in a network along with perform emitter detection, classification and geolocation.

Current ES systems are designed to detect enemy electronic signals by extracting Pulse Descriptor Words (PDWs) from these signals. Modern radar systems used by adversaries increasingly employ Artificial Intelligence (AI) and it is becoming increasingly difficult to identify, characterize, and represent their waveforms using PDWs alone. To deploy effective Electronic Countermeasures (ECM), there is a need to develop novel methods, including machine learning (ML) [1,2] to enable dynamic and scalable data representation and compression methods that represent complex waveforms, which can be efficiently shared with other nodes in the network. The main goal of this project is to develop advanced techniques for dynamic and scalable signal representation to describe complex waveforms in a compressed format for radio frequency (RF) spectrum knowledge sharing and support network level information dissemination for emitter detection, classification and geolocation representative of activity segmentation [3]. Operational size, weight and power (SWaP) constraints should be determined to implement the proposed algorithms on hardware platforms and the developed techniques should be evaluated and demonstrated with hardware-in-the-loop experiments in relevant spectrum environments.

As this is a Direct-to-Phase II (D2P2) topic, no Phase I awards will be made. The offeror is required to provide detail and documentation in the Direct to Phase II proposal which demonstrates accomplishment of a “Phase I-like” effort, including a feasibility study. This includes determining, insofar as possible, the scientific and technical merit and feasibility of ideas appearing to have commercial potential. It must have validated the product-market fit between the proposed solution and a potential Air Force stakeholder. The offeror should have defined a clear, immediately actionable plan with the proposed solution and the AF customer. Relevant areas of demonstrated experience and success include high-fidelity M&S, solutions to complex RF problems using AI/ML, concept development, solution demonstration and product evaluation.

Eligibility for a Direct to Phase Two (D2P2) is predicated on the offeror having performed a “Phase I-like” effort predominantly separate from the SBIR/STTR Programs. These efforts will include M&S, simulation of prototype concepts, cost benefit analysis, system-of-systems studies, experimentation and evaluation of operational imperatives to enable future concepts. Prototypes, M&S and experimentation should explore a wide range of integrating commercial capabilities to support the operational imperatives. These capabilities should consider areas that are unique to military operations, logistics, mission planning, mission execution, base sustainment and logistics.An important goal for Phase II effort is to demonstrate the baseline and proposed PDW extraction methods and analyze performance through both simulation and hardware experiments. Experiments should address military-unique requirements that may not be otherwise met by commercial capabilities.

Phase III shall include upgrades to the analysis, M&S, T&E results and provide mature prototypes of system concepts. Phase III shall provide a business plan and address the ability to transition technology and system concepts to commercial applications. The adapted non-Defense commercial solutions shall provide expanded mission capability for a broad range of potential Governmental and civilian users and alternate mission applications. Integration and other technical support to operational users may be required.