Transfer learning with convolutional neural networks for moving target classification with micro-Doppler radar spectrograms

Esra Al Hadhrami, Maha Al Mufti, Bilal Taha, Naoufel Werghi

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

35 Scopus citations

Abstract

In this work, we propose a transfer learning approach with Convolutional Neural Networks (CNNs) for radar Automatic Target Recognition (ATR). Radar echo signals of moving targets introduce micro-Doppler signatures that are widely used in classifying moving targets. Spectrograms have the advantage of expressing the distinctive micro-Doppler signatures of different targets, and thus fed as 2D images to a CNN model. A pre-trained CNN model namely AlexNet is employed as a feature extractor in which feature maps can be extracted from any of the layers to train a classical classifier. SoftMax classifier have been used in this approach. The efficiency of the presented framework is demonstrated on the public RadEch database of 8 ground moving target classes, in which the experimental results indicate that our methodology significantly outperforms other competitive state-of-the-art methods with an accuracy of 99.9%.

Original languageBritish English
Title of host publication2018 International Conference on Artificial Intelligence and Big Data, ICAIBD 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages148-154
Number of pages7
ISBN (Electronic)9781538669877
DOIs
StatePublished - 25 Jun 2018
Event2018 International Conference on Artificial Intelligence and Big Data, ICAIBD 2018 - Chengdu, China
Duration: 26 May 201828 May 2018

Publication series

Name2018 International Conference on Artificial Intelligence and Big Data, ICAIBD 2018

Conference

Conference2018 International Conference on Artificial Intelligence and Big Data, ICAIBD 2018
Country/TerritoryChina
CityChengdu
Period26/05/1828/05/18

Keywords

  • AlexNet
  • Automatic target recognition
  • Convolutional neural network
  • Micro-doppler
  • Radar classification
  • Transfer learning

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