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Toward Robust Receiver-Invariant Specific Emitter Identification via Multi-Task Adversarial Learning
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Summary
MTL-SEI, a multi-task learning framework for Specific Emitter Identification, addresses shortcut learning under cross-receiver distribution shifts, where networks rely on receiver-specific artifacts instead of transmitter fingerprints. It combines spectrum-based feature extraction, receiver-invariant adversarial training via a gradient reversal layer, equalization-state prediction, and uncertainty-guided task weighting. On the ManySig dataset under a receiver-disjoint protocol, it reaches 88.50% transmitter identification accuracy, a 37.7% improvement over the 1D-CNN baseline and over 6.92% average gain over state-of-the-art domain generalization methods.