  Specific emitter identification (SEI) is a highly potential technology for
physical layer authentication that is one of the most critical supplement for
the upper-layer authentication. SEI is based on radio frequency (RF) features
from circuit difference, rather than cryptography. These features are inherent
characteristic of hardware circuits, which difficult to counterfeit. Recently,
various deep learning (DL)-based conventional SEI methods have been proposed,
and achieved advanced performances. However, these methods are proposed for
close-set scenarios with massive RF signal samples for training, and they
generally have poor performance under the condition of limited training
samples. Thus, we focus on few-shot SEI (FS-SEI) for aircraft identification
via automatic dependent surveillance-broadcast (ADS-B) signals, and a novel
FS-SEI method is proposed, based on deep metric ensemble learning (DMEL).
Specifically, the proposed method consists of feature embedding and
classification. The former is based on metric learning with complex-valued
convolutional neural network (CVCNN) for extracting discriminative features
with compact intra-category distance and separable inter-category distance,
while the latter is realized by an ensemble classifier. Simulation results show
that if the number of samples per category is more than 5, the average accuracy
of our proposed method is higher than 98\%. Moreover, feature visualization
demonstrates the advantages of our proposed method in both discriminability and
generalization. The codes of this paper can be downloaded from
GitHub(https://github.com/BeechburgPieStar/Few-Shot-Specific-Emitter-Identification-via-Deep-Metric-Ensemble-Learning)
