NAVAIR & NAVSEA Open Topic for Counter Unmanned Air Systems (C-UAS) is sponsored by Department of Defense, Navy. This opportunity supports mission-aligned projects and measurable outcomes.

NAVAIR & NAVSEA Open Topic for Counter Unmanned Air Systems (C-UAS) Topic Number: DON26BX03-NP002 Solicitation Number: 26. BX Solicitation Status: Open NOTE: The Solicitations and topics listed on this site are copies from the various SBIR agency solicitations and are not necessarily the latest and most up-to-date.

For this reason, you should use the agency link listed below which will take you directly to the appropriate agency server where you can read the official version of this solicitation and download the appropriate forms and rules.

View Official Solicitation July 22, 2026; September 25, 2026; December 23, 2026 Unmanned Aerial Systems (UAS) present a complex, multi-dimensional challenge that affects everything from individual ship protection to broad strategic operations in the U.S. Navy.

The proliferation of cheap, easily accessible, and increasingly sophisticated unmanned systems by both state adversaries and non-state actors has created an urgent and evolving threat landscape. Events in the Middle East, where Navy vessels have been actively engaging drones, have transformed these threats from a theoretical problem into a daily operational reality.

The nature of the UAS threat for Naval installations, Naval aircraft, and Naval ships is diverse and rapidly changing, creating a significant challenge for Naval defenses. C-UAS is enabled by secure communication and information technology and includes technologies to Detect, Track, Identify, Assess, and Neutralize single and swarms of UAS from Air, Sea, or Ground leveraging Manned or Unmanned platforms.

Primary technology areas of interest are listed below; however, solutions outside these areas will be considered. Please indicate the technology area of interest within the Technical Abstract section of the online proposal Cover Sheet Volume, Volume 1.

• AI-Powered Target Recognition for C-UAS: Develop and train machine learning object detection algorithms for the real-time classification and identification of UAS threats from various sensor inputs, including imagery and RF signatures.

• AI/ML-Enhanced Swarm Detection, Tracking, and Anomalies: Develop an AI/ML-enhanced monitoring framework that integrates advanced object detection with behavioral analysis to detect, track, and predict the collective intent of diverse drone swarms in real time. The framework should evaluate potential risks by establishing behavioral baselines and assessing factors such as flight paths, payloads, and proximity to sensitive areas.

By identifying real-time anomalies, such as deviations in trajectory or formation, the framework will serve as a primary trigger for risk escalation and the deployment of protective measures, providing a sophisticated solution for managing the complexities of coordinated swarm behavior.

• Non-Kinetic / Low Kinetic Defeat System for Small UAS: Develop a lightweight system with a focus on minimizing size, weight, and power requirements for integration on Naval platforms for the non-kinetic/low kinetic defeat of Group 1 and 2 UAS at tactically relevant ranges. • AI/ML for Countering Adaptive Programmable Camouflage: Develop an AI/ML framework to specifically detect threats employing programmable camouflage.

This framework will overcome the "target novelty" problem by continuously adapting to previously unseen camouflage patterns during a mission. The core requirement is for the system to increase its detection accuracy against these evolving threats, providing a decisive countermeasure to adversaries attempting to visually cloak their assets to evade traditional sensors.