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A Deep Learning Method of Moving Target Classification in Clutter Background

  • Ningyuan Su
  • Xiaolong ChenEmail author
  • Xiaoqian Mou
  • Lin Zhang
  • Jian Guan
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 517)

Abstract

The Doppler spectrums of radar echoes of targets can reflect the change of the instantaneous velocity of targets. Therefore, it can be used for analyzing the motion state of the target and classifying them. Besides, deep learning is widely used in the classification of images. This paper proposes a deep learning based method of classifying targets in sea clutter. First, we introduce the motion model of targets and analyze their Doppler spectrum, based on which, we stimulate the time–frequency images of targets’ radar echoes. Since clutters in echoes usually obey Weibull distribution, we add Weibull clutter (Mezache and Soltani) to a novel threshold optimization technique for far-away detection in Weibull clutter using fuzzy neural networks, 2007, [1]) to the echo signals. Then we classify targets with different networks using NVIDIA DIGITS, based on the images and analyze the results of classification.

Keywords

Doppler spectrum Weibull clutter Target classification Deep learning 

Notes

Acknowledgements

This work was supported in part by The National Natural Science Foundation of China (61871391, U1633122, 61871392, 61531020), Scientific Research Development of Shandong (J17KB139), and Young Elite Scientist Sponsorship Program of CAST (YESS20160115).

References

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Ningyuan Su
    • 1
  • Xiaolong Chen
    • 1
    Email author
  • Xiaoqian Mou
    • 1
  • Lin Zhang
    • 1
  • Jian Guan
    • 1
  1. 1.Radar Detection Research SectionNaval Aviation UniversityYantaiChina

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