Signal Clustering with Class-independent Segmentation
This work addresses the challenge of source separation in radar signal processing, which is incremental as it applies existing image segmentation techniques to a new domain.
The paper tackles the problem of clustering complex radar signals by proposing a deep learning method that encodes signals into images and uses image segmentation for clustering, achieving superior performance over various baselines.
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.