Threat Characterization & Machine Learning Analytics

The contract seeks the development of AI and machine learning models designed to detect and classify unknown drone radio frequency signatures through adaptive learning and anomaly detection methodologies. The focus is on creating intelligent systems capable of recognizing novel and evolving drone threats by analyzing RF patterns that deviate from known norms, enabling real-time identification in dynamic operational environments. The work will involve robust algorithmic design, training on diverse RF datasets, and continuous model refinement to maintain accuracy against emerging drone technologies. This subcontract is issued under NAICS code 541721 for research and development in physical, engineering, and life sciences, and is managed by the Department of Defense through W6QK Acc-Apg Adelphi. The performance location is designated as West Fork, Arkansas, with a posting date of July 27, 2026. Although solicitation details and point of contact information are not provided, the contract emphasizes technical innovation in threat characterization and demands capabilities aligned with national defense objectives in autonomous drone detection and classification.