Specific Emitter Identification Using Convolutional Neural Network-Based IQ Imbalance Estimators

Specific Emitter Identification is the association of a received signal to a unique emitter, and is made possible by the naturally occurring and unintentional characteristics an emitter imparts onto each transmission, known as its radio frequency fingerprint. This paper presents an approach for identifying emitters using convolutional neural networks to estimate the inphase/quadrature (IQ) imbalance parameters of each emitter, using only the received raw IQ data as input. Because an emitter's IQ imbalance parameters will not change as it changes modulation schemes, the proposed approach has the ability to track emitters, even as they change the modulation scheme. The performance of the developed approach is evaluated using simulated quadrature amplitude modulation and phase-shift keying signals, and the impact of signal-to-noise ratio, imbalance value, and modulation scheme are considered. Furthermore, the developed approach is shown to outperform a comparable feature-based approach, while making fewer assumptions and using fewer data per decision.