@article{M83E439B8,
title = "Semantic Segmentation-Based Drone Communication Signal Separation Method",
journal = "The Journal of Korean Institute of Communications and Information Sciences",
year = "2025",
issn = "1226-4717",
doi = "10.7840/kics.2025.50.11.1679",
author = "Do-Hyun Park, Soon-Young Kwon, Jinwoo Jeong, Isaac Sim, Sangbom Yun, Junghyun Seo, Hyoung-Nam Kim",
keywords = "Electronic Warfare, Deep Learning, , Signal Separation",
abstract = "Recently, the importance of accurately estimating communication signal parameters used by drones has increased for identifying drones operating for tactical purposes. However, drone communication signals often contain multiple signals transmitted concurrently within the same frequency band, making it challenging to accurately estimate the parameters of individual signals. In this paper, we propose a deep learning-based method to separate drone communication signals. The proposed approach converts the received signal into spectrogram and separates them by employing a semantic segmentation model.
Simulation results show that the proposed method accurately performs signal separation, achieving excellent performance in estimating signal parameters."
}