Toward Robust Receiver-Invariant Specific Emitter Identification via Multi-Task Adversarial Learning

This research addresses a problem where AI models trained to identify radio transmitters (specific emitter identification, or SEI) fail when tested on different hardware receivers due to shortcut learning (when models rely on irrelevant patterns instead of genuine features). The authors propose MTL-SEI, a framework that uses adversarial training (a technique where two competing AI systems help each other improve) and multiple related learning tasks to teach models to ignore receiver-specific artifacts and focus on true transmitter fingerprints, achieving 88.50% accuracy on test data.