The Department of Defense (DOD) is seeking proposals for the topic "Speedy UAV Swarms Detection, Identification, and Tracking using Deep Learning-Based Fusion Methodology for Radar and Infrared Imagers". The objective of this Small Business Innovation Research (SBIR) topic is to develop an innovative deep learning-based fusion methodology for radar and infrared cameras that can effectively detect, identify, and track unmanned aerial vehicle (UAV) swarms with high probability of detection and low probability of false alarms. The technology is aimed at countering the security threats posed by UAVs in both defense and civilian arenas. The proposed solution should involve the fusion of data from phased array radar systems and MWIR/LWIR infrared cameras to achieve accurate and fast results in UAV swarm detection, identification, and tracking. The system should be able to handle challenges such as diverse data formats, resolutions, and phenomenologies. The performance goals include a probability of UAV swarm detection-to-track of more than 90%, a classification accuracy of more than 90%, and a probability of false alarms less than 10%. The project will be conducted in multiple phases, starting with the design and feasibility demonstration in Phase I, followed by algorithm development and testing in Phase II, and finally, the delivery of mature prototypes in Phase III. The anticipated duration of the project is not specified, but it is part of the DOD SBIR 24.1 BAA released in 2024. The funding specifics are not provided in the document. For more information and to access the solicitation, visit the DOD SBIR/STTR Opportunities website.