Unsupervised Emitter Clustering through Deep Manifold Learning
James Stankowicz, Scott Kuzdeba · 2021
We perform unsupervised clustering to group Radio Frequency signals according to the device that transmitted the signal. We do so by first performing supervised training on a RiftNet classifier that contains a layer that can be used as a latent vector. We then perform the unsupervised clustering on a completely different set of devices than those used for training. During unsupervised clustering, we project each signal into its latent vector, then perform clustering in a UMAP-learned reduction of that space. This approach provides understanding and a path forward on how such semi-supervised deep-learning clustering approaches might fit in a real world RF system.