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Topic DAF27TZ02-DV005 from the DoW STTR 2027 BAA, Release 2. Pre-release now; proposals due January 6, 2027, 12:00 PM ET.
Quick answer
DAF27TZ02-DV005 is a Department of the Air Force Direct-to-Phase II STTR topic seeking a compressed, scalable signal representation of complex radar waveforms that Electronic Support systems can share across a network for emitter detection, classification, and geolocation. No Phase I awards will be made. Proposals are due January 6, 2027, 12:00 PM ET.
Solicitation at a glance
- Topic number: DAF27TZ02-DV005
- Official title: RF Spectrum Operations Enhanced with Distributed AI-Assisted Sharing of Knowledge Sharing (RF-DASK)
- Agency: Department of the Air Force
- Program: STTR (DoW STTR 2027 BAA, Release 2)
- Award type: Direct to Phase II (no Phase I awards)
- Award amount: Up to $2,000,000. Source: Department of the Air Force Release 2 instructions in DSIP.
- CMMC level: Level 2 (Self)
- ITAR: Yes. Export-controlled (ITAR/EAR); see topic text
- Technology areas: Electronics, Sensors
- Pre-release (direct TPOC contact allowed): November 4 to December 2, 2026, 8:00 AM ET
- Opens in DSIP: December 2, 2026, 8:00 AM ET
- Proposals due: January 6, 2027, 12:00 PM ET
- Official source: DSIP Topics app (search DAF27TZ02-DV005)
BW&CO's take
What the Department of the Air Force wants
Electronic Support receivers describe threat radars with Pulse Descriptor Words, but adversary radars that use AI are getting harder to represent that way. The Air Force wants new methods, including machine learning, that compress complex waveforms into a dynamic, scalable representation that can be shared efficiently between networked ES nodes and used to control Electronic Countermeasures. The solution must support distributed network operation and perform emitter detection, classification, and geolocation. The topic also calls for determining operational SWaP constraints for hardware implementation and demonstrating the techniques with hardware-in-the-loop experiments in relevant spectrum environments. Phase II should demonstrate the baseline and proposed PDW extraction methods and analyze performance in both simulation and hardware experiments.
Who is a good fit
- Prior AI/ML work on radar emitter identification, classification, or pulse activity segmentation using PDW or raw RF data
- Signal representation or compression methods suited to sharing spectrum knowledge between distributed sensor nodes
- High-fidelity modeling and simulation plus hardware-in-the-loop RF experimentation
- An existing relationship with an Air Force stakeholder who has validated the need for the proposed solution
Watch-outs
- This is Direct-to-Phase II: the proposal must document a completed Phase I-like feasibility effort performed predominantly outside the SBIR/STTR programs, including validated product-market fit with a potential Air Force stakeholder and an immediately actionable plan with the AF customer.
- The metadata lists this as an STTR topic. STTR awards involve a research-institution partner, so plan the team accordingly.
- The topic metadata marks it as ITAR-restricted, which generally limits foreign national participation.
- The topic metadata lists CMMC Level 2 (Self).
Think your company fits DAF27TZ02-DV005? Talk with BW&CO before the January 6 deadline.
Plan Your DAF27TZ02-DV005 Submission
Official topic text (verbatim from DSIP)
The text in this section is copied word for word from the official DSIP topic as published on November 4, 2026. DSIP is the official source and topics can be amended. Always confirm the current version at dodsbirsttr.mil before you submit. Our commentary above is BW&CO's interpretation, not Government guidance.
Title
RF Spectrum Operations Enhanced with Distributed AI-Assisted Sharing of Knowledge Sharing (RF-DASK)
Objective
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.
Description
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.
Phase I
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.
Phase II
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 Dual Use Applications
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.
References
S. Apfeld, A. Charlish, “Recognition of Unknown Radar Emitters with Machine Learning,” IEEE Transactions on Aerospace and Electronic Systems Vol. 57, No. 6, 2021.
M. Demircan and A. G. Pakfiliz, “A Novel Machine Learning Approach for Optimizing Radar Warning Receiver Preprogramming,” Acadlore Trans. Mach. Learn., vol. 3, no. 4, pp. 202-213, 2024.
Z. Huang, A. Pemasiri, S. Denman, C. Fookes and T. Martin, “Multi-Stage Learning for Radar Pulse Activity Segmentation,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7340-7344, 2024.
Keywords
Radar, Pulse Descriptor Words, Artificial Intelligence, Machine Learning, Deep Learning Neural Networks, Emitter Identification, Classification, Geolocation
Frequently asked questions
What is DAF27TZ02-DV005?
It is a Department of the Air Force STTR topic (RF-DASK) to develop a dynamic, scalable, compressed representation of complex radar waveforms for sharing RF spectrum knowledge between Electronic Support systems, supporting distributed emitter detection, classification, geolocation, and control of Electronic Countermeasures.
When is DAF27TZ02-DV005 due?
Proposals are due January 6, 2027, 12:00 PM ET. DSIP pre-release began November 4, 2026, and direct contact with the topic authors is allowed only until the topic opens on December 2, 2026, 8:00 AM ET.
Is this a Phase I or Direct-to-Phase II topic?
It is Direct-to-Phase II. No Phase I awards will be made, and the proposal must document a Phase I-like effort, including a feasibility study, performed predominantly separate from the SBIR/STTR programs.
What has to be demonstrated in Phase II?
Phase II should demonstrate the baseline and proposed PDW extraction methods and analyze performance through both simulation and hardware experiments. The topic also calls for determining operational SWaP constraints and demonstrating the techniques with hardware-in-the-loop experiments in relevant spectrum environments.