Electromagnetic Signal Classification Using Ensembles (E-CUE)

US military forces have encountered increasingly complex electromagnetic (EM) environments over the last two decades. This complicates the task of rapidly and properly classifying emitters, which is critical to the safe, effective operation of combat platforms. Many large military platforms such as warships and aircraft rely on electronic warfare support (ES) systems for self-defense. Platforms conducting missions such as covert strike, cooperative targeting, and tactical engagement (e.g., anti-surface warfare (ASUW)) require ES capabilities in addition to those required for self-defense, since they must intercept, identify, and locate or localize emitters of interest. To rapidly and correctly fingerprint sophisticated emitters, we propose to design and demonstrate an ES system classification tool, Electromagnetic Signal Classification Using Ensembles (E-CUE). E-CUE incorporates several novel capabilities, including signal features based on alternative characterizations, classification ensembles, characterization of classification uncertainty and reliability, emitter correlation and fusion, adaptability through process assessment (PA) and process management (PM), and improved early warning.