  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 work presents an approach for
identifying emitters using Convolutional Neural Networks to estimate the IQ
imbalance parameters of each emitter, using only 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 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. Further, the developed approach is shown to
outperform a comparable feature-based approach, while making fewer assumptions
and using less data.
