Moving Target Classification Based on micro-Doppler Signatures Via Deep Learning

Yonatan D. Dadon, Shahaf Yamin, Stefan Feintuch, Haim H. Permuter, Igal Bilik, Joseph Taberkian

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations


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.

Original languageEnglish
Title of host publication2021 IEEE Radar Conference
Subtitle of host publicationRadar on the Move, RadarConf 2021
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Electronic)9781728176093
StatePublished - 7 May 2021
Event2021 IEEE Radar Conference, RadarConf 2021 - Atlanta, United States
Duration: 8 May 202114 May 2021

Publication series

NameIEEE National Radar Conference - Proceedings
ISSN (Print)1097-5659


Conference2021 IEEE Radar Conference, RadarConf 2021
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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