Fusion of Cooperative Information Sharing for Uncrewed Air Systems (FOCISAUS)

STTR topic DAF27TZ01-DV003 (U.S. Department of Defense – Air Force), Phase II. Open to U.S. small businesses; STTR projects partner with a U.S. research institution. Opens: October 28, 2026. Closes: November 25, 2026. 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…