SEI-SHIELD achieves state-of-the-art accuracy on POWDER and ORACLE datasets under label noise by using MoCo pre-training for label-independent features followed by neighborhood consistency filtering and confidence-based rescue of hard samples.
Adaptive decomposi- tion and extraction network of individual fingerprint features for specific emitter identification,
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SEI-SHIELD: Robust Specific Emitter Identification Under Label Noise Via Self-Supervised Filtering and Iterative Rescue
SEI-SHIELD achieves state-of-the-art accuracy on POWDER and ORACLE datasets under label noise by using MoCo pre-training for label-independent features followed by neighborhood consistency filtering and confidence-based rescue of hard samples.