  Radar signals have been dramatically increasing in complexity, limiting the
source separation ability of traditional approaches. In this paper we propose a
Deep Learning-based clustering method, which encodes concurrent signals into
images, and, for the first time, tackles clustering with image segmentation.
Novel loss functions are introduced to optimize a Neural Network to separate
the input pulses into pure and non-fragmented clusters. Outperforming a variety
of baselines, the proposed approach is capable of clustering inputs directly
with a Neural Network, in an end-to-end fashion.
