Description
Objective: The DON is seeking proposals for enhancing existing prototypes or concepts to improve C-UAS operations and demonstrate a novel, highly effective, and scalable capability to counter the rapidly proliferating threat of hostile UAS. Adversaries are increasingly leveraging low-cost, autonomous, and often numerous UAS to gain an asymmetric advantage, conduct surveillance, and execute kinetic attacks, posing a significant risk to U.S. and allied forces, critical infrastructure, and mission success. This SBIR Open Topic seeks innovative solutions that can detect, track, identify, and neutralize single and multiple UAS threats in complex operational environments, ultimately providing the warfighter with a decisive overmatch capability to ensure freedom of maneuver and protection of assets. Description: 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. Keywords: counter-UXS or C-UXS, counter-UAS or C-UAS; counter-drone; Anti-Drone; Drone Defense; Drone Security; UAS Mitigation & Neutralization; Air Domain Awareness; AI Powered C2 for C-UAS; AI Sensor Fusion; AI/ML Enhanced Detection & Tracking; Swarm Tracking; Adaptive Programmable Camouflage CMMC Level: Level 2 (Self)