@inproceedings{c0c1d51e66f7406a938bd0323c628e2d,
title = "Moving Target Classification Based on micro-Doppler Signatures Via Deep Learning",
abstract = "Radar-based classification of ground moving targets relies on Doppler information. Therefore, the classification between humans and animals is a challenging task due to their similar Doppler signatures. This work proposes a Deep Learning-based approach for ground-moving radar targets classification. The proposed algorithm learns the radar targets' micro-Doppler signatures in the 2D fast-time slow-time radar echoes domain. This work shows that the convolutional neural network (CNN) can achieve high classification performance. Also, it shows that efficient data augmentation and regularization significantly improve classification performance and reduce over-fit.",
author = "Dadon, {Yonatan D.} and Shahaf Yamin and Stefan Feintuch and Permuter, {Haim H.} and Igal Bilik and Joseph Taberkian",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Radar Conference, RadarConf 2021 ; Conference date: 08-05-2021 Through 14-05-2021",
year = "2021",
month = may,
day = "7",
doi = "https://doi.org/10.1109/RadarConf2147009.2021.9455270",
language = "American English",
series = "IEEE National Radar Conference - Proceedings",
booktitle = "2021 IEEE Radar Conference",
}