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Recognition and Classification of Rotorcraft by Micro-Doppler Signatures Using Deep Learning

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10860)

Abstract

Detection and classification of rotorcraft targets are of great significance not only in civil fields but also in defense. However, up to now, it is still difficult for the traditional radar signal processing methods to detect and distinguish rotorcraft targets from various types of moving objects. Moreover, it is even more challenging to classify different types of helicopters. As the development of high-precision radar, classification of moving targets by micro-Doppler features has become a promising research topic in the modern signal processing field. In this paper, we propose to use the deep convolutional neural networks (DCNNs) in rotorcraft detection and helicopter classification based on Doppler radar signals. We apply DCNN directly to raw micro-Doppler spectrograms for rotorcraft detection and classification. The proposed DCNNs can learn the features automatically from the micro-Doppler signals without introducing any domain background knowledge. Simulated data are used in the experiments. The experimental results show that the proposed DCNNs achieve superior accuracy in rotorcraft detection and superior accuracy in helicopter classification, outperforming the traditional radar signal processing methods.

Keywords

Convolutional neural network Deep learning Target detection Classification Micro-Doppler 

Notes

Acknowledgement

This project was partially supported by Grants from Natural Science Foundation of China #71671178/#91546201/#61202321, and the open project of the Key Lab of Big Data Mining and Knowledge Management. It was also supported by Hainan Provincial Department of Science and Technology under Grant No. ZDKJ2016021, and by Guangdong Provincial Science and Technology Project 2016B010127004.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer and ControlUniversity of Chinese Academy of SciencesBeijingChina
  2. 2.Key Lab of Big Data Mining and Knowledge ManagementChinese Academy of SciencesBeijingChina

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