The Department of Defense, specifically the United States Air Force, is seeking innovative solutions to detect uncrewed aircraft systems (UAS) operating in cluttered environments, as outlined in the SBIR opportunity titled "Detection of Uncrewed Aircraft Systems in Cluttered Environments." The objective is to develop effective sensor technologies capable of autonomously identifying small UAS threats that fly low to the ground and are obscured by obstacles such as trees and buildings, with a focus on achieving at least 80% detection effectiveness and minimizing false alarms. This initiative is critical for enhancing air defense capabilities at military installations, particularly as UAS technology evolves and poses new challenges. Interested parties should note that the opportunity will be open for applications from May 28, 2025, to June 25, 2025, with further details available at the provided source link.

## Description

Military installations are often surrounded by cluttered environments which can now be exploited by autonomous and semi-autonomous Uncrewed Aircraft Systems (UAS). Advances in intelligent navigation systems allow UAS threats to fly low to the ground through trees and between buildings to sneak into unauthorized areas and surveil or attack high value targets on the ground with little to no warning. Existing electro-optical, infrared, radar,and lidar systems cannot effectively detect these threats when flying low to the ground in clutter, especially when obscured in the foreground by clutter. This challenge is further exacerbated by emerging capabilities for small, inexpensive drones to fly together in large, coordinated &ldquo;swarms&rdquo; to intelligently avoid detection and overwhelm defenses. Air Combat Command&rsquo;s J3 Operations Directorate and J4 Logistics Directorate have joint responsibility for Counter-UAS (C-UAS) activities and Air Base Air Defense. ACC is highly interested in novel approaches to detect low flying, relatively small and slow UAS threats in cluttered environments. It is believed that emerging work in acoustics and wireless/RF signal sensors, as well as artificial intelligence and machine learning, may offer a solution to the difficult C-UAS detection problem by &ldquo;seeing&rdquo; through trees and buildings. Proposed solutions need to provide a cost-effective method for detection of UAS threats for large military installations with vast perimeters to defend. To be effective, the solutions must be able to distinguish between actual threats and false alarms such as birds. It is acceptable to trade off range by, for example, operating in a mesh network. The new technology will ultimately need to communicate with traditional sensor systems in a layered defense architecture.

## Objective

Provide an effective sensor technology to detect small uncrewed aircraft systems autonomously flying low to the ground through cluttered environments (obscured in the foreground by trees or buildings).

## Phase I

Develop a feasibility study that identifies limitations of available sensor technology to detect small UAS in clutter, evaluates potential novel sensor technologies and methodologies, and proposes an acceptable development path.

## Phase II

Deliver a prototype and demonstrate the capability to effectively detect and track small UAS threats obscured by cluttered environments with at least 80% effectiveness and less than 20% false alarms with a cost-effective preliminary concept of operation for large-scale coverage to defend a typical military installation (TRL 6). The prototype need not be able to determine the type or size of UAS.

## Phase III: Dual Use Applications

Deliver a fully-functional product with the capability to effectively detect and track small UAS threats obscured by cluttered environments with at least 95% effectiveness and less than 5% false alarms with a cost-effective concept of operation for large-scale coverage to defend a typical military installation (advance from TRL 6 to TRL 9 maturity). The product development roadmap should strive to expeditiously add the capability to integrate with traditional sensor arrays, guide counter-measures, and determine the type and size of UAS.

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