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.
@article{arxiv.1911.07590,
title = {Signal Clustering with Class-independent Segmentation},
author = {Stefano Gasperini and Magdalini Paschali and Carsten Hopke and David Wittmann and Nassir Navab},
journal= {arXiv preprint arXiv:1911.07590},
year = {2023}
}