@inproceedings{688544b453f442a5a892dac04d8302a2,
title = "Classification of ground moving radar targets using convolutional neural network",
abstract = "In this paper, we propose a new approach for Pulsed Doppler Radar Automatic Target Recognition (ATR). Target classification depends highly on the quality of the training database, the extracted features and the classification algorithm. Radar echo signals captured by the Radar show the Doppler effect produced by moving targets. Those echo signals can be processed in different domains to attain distinctive characteristics of targets that can be used for target classification. The proposed approach is based on utilizing a pre-Trained Convolutional Neural Network (CNN) as a feature extractor whereas the output features are used to train a multiclass Support Vector Machine (SVM) classifier. Our approach was tested on RadEch database of 8 ground moving targets classes. Our approach outperformed the state-of-The-Art methods, using the same database, and reached an accuracy of 99\%.",
keywords = "Automatic target recognition, Convolutional Neural Network, Radar classification, Transfer learning",
author = "Hadhrami, \{Esra Al\} and Mufti, \{Maha Al\} and Bilal Taha and Naoufel Werghi",
note = "Publisher Copyright: {\textcopyright} 2018 Warsaw University of Technology.; 22nd International Microwave and Radar Conference, MIKON 2018 ; Conference date: 14-05-2018 Through 17-05-2018",
year = "2018",
month = jul,
day = "5",
doi = "10.23919/MIKON.2018.8405154",
language = "British English",
series = "MIKON 2018 - 22nd International Microwave and Radar Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "127--130",
booktitle = "MIKON 2018 - 22nd International Microwave and Radar Conference",
address = "United States",
}