Improved Specific Emitter Identification Based on Margin Disparity Discrepancy in Varying Modulation Scenarios

Abstract

In Specific Emitter Identification (SEI), transmitters are typically distinguished through Radio Frequency Fingerprint (RFF) features. However, modulation schemes can be deliberately coupled to confound RFF information. This paper addresses modulation variation as a Domain Adaptation (DA) problem and proposes an SEI framework based on Margin Disparity Discrepancy (MDD) to enhance robustness in modulation-varying scenarios. Specifically, we first establish a theoretical tight upper bound for the discrepancy between modulation domains using MDD theory. Then, we design an adversarial network to align variable features to shorten the discrepancy between modulations. Finally, we experimented with complex modulated signals including digital and analog modulation. Numerical results indicate that our approach achieves an average improvement of over 20% in accuracy compared to classical SEI methods and outperforms traditional DA techniques.