DoW/USAF · DoW STTR 2027 BAA
DAF27TZ01-DV003 — Fusion of Cooperative Information Sharing for Uncrewed Air Systems (FOCISAUS)
Open | released 2026-10-07 | opens 2026-10-28 | closes 2026-11-25 | CMMC Level 2 (Self), ITAR, STTR
DSIP topics app (search DAF27TZ01-DV003) · no SBIR.gov page at the moment
Keywords: cooperative perception; sensor fusion; data fusion; uncrewed air systems; autonomy; adaptive transmission; compression; edge processing; distributed sensing; situational awareness; object detection
Technology areas: Bio Medical, Sensors
Objective
The Department of the Air Force seeks mature, innovative solutions ready for prototype development and demonstration in cooperative perception for uncrewed or autonomous air systems. The objective of this topic is to develop and demonstrate a scalable prototype that enables distributed sensing nodes to share, prioritize, and fuse mission-relevant information in near real time to improve object detection, tracking, navigation, and operator situational awareness in cluttered and bandwidth-constrained environments.
Solutions should support efficient information exchange, adaptive transmission under changing network conditions, and a credible data fusion design that converts distributed sensor observations into actionable fused products. Offerors should propose technologies with demonstrated Phase I-type maturity and a clear path to prototype integration, testing, and transition.
Description
Distributed air operations increasingly depend on timely awareness of objects, hazards, and activities beyond the field of view of any individual platform. Cooperative perception offers a path to extend sensing coverage and reduce blind spots by allowing multiple platforms and supporting nodes to share sensor-derived information. Yet this benefit is limited by the large size of modern sensor data, the variability of wireless links, the need for rapid decision making, and the difficulty of combining observations from different platforms, geometries, and sensing modalities.
This topic seeks prototype-ready solutions that build on prior feasibility work in distributed information sharing, compression, adaptive transmission, and sensor data fusion. The government is interested in capabilities that can select and transmit the most relevant information, manage latency and bandwidth constraints, and fuse data from multiple sources into improved perception outputs for autonomy or operator use.
Offerors should address the data fusion aspect as a core requirement of the overall solution, whether through point cloud fusion, image or radar correlation, feature or object fusion, track association, or other mission-appropriate methods. Solutions should also describe how fused outputs are exposed to mission applications, edge processing nodes, or user interfaces. Approaches that can operate under degraded or intermittent communications and can scale across multiple sensing platforms are of particular interest.
Prototype capabilities may include, but are not limited to: Distributed organization, naming, publication, discovery, or retrieval of sensor information; Adaptive data selection, fidelity control, compression, and transmission policies; Real-time or near-real-time fusion and correlation of data from multiple platforms or sensor types; Delivery of fused perception products to autonomy software, edge analytics, or mobile mission applications; and Measurable improvements in latency, bandwidth efficiency, blind-spot reduction, and detection or tracking performance.
Phase I
This topic is intended for technologies that are sufficiently mature to proceed directly into prototype development. Therefore, Phase I awards will not be made for this topic and Phase I proposals will not be accepted. Offerors must provide detail and documentation in the D2P2 proposal demonstrating completion of a Phase I-type effort. This should include evidence of scientific and technical feasibility, relevance to a Department of the Air Force need, and a clear basis for moving directly into integrated prototype development.
The "Phase As this is a Direct-to-Phase-II (D2)2) topic, no Phase I proposals will be accepted and no Phase I awards will be made under this topic. Offerors responding to this topic shall demonstrate “Phase I-type” technical capability through prior research, development, laboratory validation, commercial development, or operational demonstrations performed outside of the SBIR/STTR program. This should include evidence of scientific and technical feasibility, relevance to a Department of the Air Force need, and a clear basis for moving directly into integrated prototype development.
The “Phase I-type” effort should have:
Identified representative operational needs and stakeholders for cooperative perception and distributed situational awareness;
Demonstrated technical feasibility in distributed information exchange, sensor-data management, compression, or adaptive transmission;
Established an initial and credible data fusion approach appropriate to the mission problem;
Produced evidence of prototype-readiness through modeling, simulation, laboratory integration, or early demonstration; and5
Described a clear path to Phase II integration, testing, and transition.I-type" effort should have:1. Identified representative operational needs and stakeholders for cooperative perception and distributed situational awareness;2. Demonstrated technical feasibility in distributed information exchange, sensor-data management, compression, or adaptive transmission;3. Established an initial and credible data fusion approach appropriate to the mission problem;4. Produced evidence of prototype-readiness through modeling, simulation, laboratory integration, or early demonstration; and 5. Described a clear path to Phase II integration, testing, and transition.
Phase II
Phase II will focus on developing and demonstrating an integrated prototype for cooperative perception and sensor data fusion using distributed air and ground sensing nodes. The prototype should build on validated prior work and demonstrate how shared sensor information can be selectively transmitted, fused, and delivered to mission applications with measurable operational value.
Phase II efforts may include implementation of distributed data-sharing mechanisms; adaptive policies for what information to exchange and at what fidelity; integration of compression, decompression, or bandwidth-management functions; real-time or near-real-time fusion of representative sensor products; delivery of fused outputs to edge or user applications; and evaluation of performance under representative wireless and mission conditions.
Key deliverables may include: A functioning prototype demonstrating cooperative perception among multiple sensing nodes; Fusion-enabled outputs delivered to an autonomy, edge, or user-facing application; Performance results addressing latency, bandwidth efficiency, perception quality, and mission relevance; and System documentation and a transition plan for operational experimentation or integration.
Phase III / dual use
Phase III efforts will focus on transition, operational deployment, and scaling of prototype cooperative perception capabilities. Defense applications include counter-UAS, tactical reconnaissance, base and perimeter security, special operations, disaster response, search and rescue, and other missions requiring distributed awareness from mobile sensors. Commercial applications include public safety, infrastructure monitoring, industrial inspection, autonomous delivery, emergency response, and remote operations where shared perception and efficient data fusion provide value.
References
- Kim, S.-W., Chong, Z. J., Qin, B., Shen, X., Cheng, Z., Liu, W., and Ang, M. H., “Cooperative perception for autonomous vehicle control on the road: Motivation and experimental results,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2013.
- Chen, Q., Tang, S., Yang, Q., and Fu, S., “Cooper: Cooperative Perception for Connected Autonomous Vehicles Based on 3D Point Clouds,” IEEE International Conference on Distributed Computing Systems (ICDCS), 2019.
- Aoki, S., Higuchi, T., and Altintas, O., “Cooperative Perception with Deep Reinforcement Learning for Connected Vehicles,” IEEE Intelligent Vehicles Symposium (IV), 2020.
- Liu, Y.-C., Tian, J., Glaser, N., and Kira, Z., “When2com: Multi-Agent Perception via Communication Graph Grouping,” CVPR, 2020.
- Hu, Y., Fang, S., Lei, Z., Zhong, Y., and Chen, S., “Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps,” NeurIPS, 2022.
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