Radar Signal Recognition Based on Transfer Learning and Feature Fusion

  • Yihan Xiao
  • Wenjian Liu
  • Lipeng GaoEmail author


This study proposes a system for the automatic recognition of radar waveforms. This system mainly uses the obvious difference in Choi–Williams distribution (CWD) images of different modulated signals. We successfully convert problems related to radar signal recognition into problems related to image recognition. The classification system uses CWD time–frequency analysis of the detected radar signal to obtain its CWD image, which can be recognized by deep neural networks. To verify this method, a database containing 1800 images and 8 types of radar signal CWD images is established. Although a convolutional neural network exhibits strong expression, it is not suitable for training a small-scale database. To solve this inadequacy, an image classification algorithm based on transfer learning and design experiments is proposed. This algorithm is intended to fine-tune three different pre-training models. This study also integrates the texture features of the image with the depth features extracted using the depth neural network to compensate for the shortcomings of the depth features in expressing image information. The simulation results indicate that the method can still be used to effectively recognize radar signals at a low SNR.


Waveform recognition Transfer learning CWD picture Convolutional neural network Feature fusion 



  1. 1.
    Tang B, Tu Y, Zhang S et al (2018) Digital signal modulation classification with data augmentation using generative adversarial nets in cognitive radio networks[J]. IEEE Access PP(99):1Google Scholar
  2. 2.
    Ding G, Wu Q, Zhang L et al (2018) An amateur drone surveillance system based on cognitive internet of things. IEEE Commun Mag 56(1):29–35CrossRefGoogle Scholar
  3. 3.
    Chen G, Zhu W (2012) Signal denoising using neighbouring dual-tree complex wavelet coefficients. IET Signal Process 6(2):143–147MathSciNetCrossRefGoogle Scholar
  4. 4.
    Tian R, Zhang G, Zhou R, et al (2016) Detection of Polyphase codes radar signals in low SNR. Math Probl Eng: 1–6MathSciNetzbMATHGoogle Scholar
  5. 5.
    Lin Y, Wang C, Wang J et al (2016) A novel dynamic Spectrum access framework based on reinforcement learning for cognitive radio sensor networks[J]. Sensors 16(10):1–22CrossRefGoogle Scholar
  6. 6.
    Zhang T, Pan W, Zou X et al (2013) High-spectral-efficiency photonic frequency Down-conversion using optical frequency comb and SSB modulation. Photon J 5(2):1–7Google Scholar
  7. 7.
    Zhao KK, Yang CZ (2016) LPI radar signal identification based on WCPF and FRFT. Modern Defence Technol 44(3):56–59MathSciNetGoogle Scholar
  8. 8.
    PHILLIP E P (2012) Detecting and classifying low probability of intercept radar (second edition) Norwood, MA, USA, Artech house: 92–111Google Scholar
  9. 9.
    Ho KM, Vaz C, Daut DG (2010) Automatic classification of amplitude, frequency and phase shift keyed signals in the wavelet domain IEEE Sarnoff symposium. New Jersey, USA IEEE Press:1–6Google Scholar
  10. 10.
    Zhu J, Zhao Y, Tang J (2013) Automatic recognition of radar signals based on time-frequency image character. Radar Conference, IET International, IET: 1–6Google Scholar
  11. 11.
    Lundén J, Koivunen V (2007) Automatic radar waveform recognition. IEEE J Select Topics Signal Process 1(1):124–136CrossRefGoogle Scholar
  12. 12.
    Xiao S, Pan T, Ren FJ (2016) Facial expression recognition using ROI-KNN deep convolutional neural networks. Acta Automat Sin 42(6):883–891Google Scholar
  13. 13.
    Xue Z, Wang J, Ding G et al (2018) Device-to-device communications underlying UAV-supported social networking. IEEE Access 6(1):34488–34502CrossRefGoogle Scholar
  14. 14.
    LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444CrossRefGoogle Scholar
  15. 15.
    George D, Shen H, Huerta E (2017) Deep transfer learning: A new deep learning glitch classification method for advanced ligo arXiv preprint arXiv:1706.07446Google Scholar
  16. 16.
    Zhang M, Liu L, Diao M (2016) LPI radar waveform recognition based on time-frequency distribution. Sensors 16(10):1682CrossRefGoogle Scholar
  17. 17.
    Liu Y, Xiao P, Wu H et al (2015) LPI radar signal detection based on radial integration of Choi-Williams time-frequency image. J Syst Eng Electron 26(5):973–981CrossRefGoogle Scholar
  18. 18.
    Lin Y, Zhu X, Zheng Z et al (2017) The individual identification method of wireless device based on dimensionality reduction and machine learning. J Supercomput (5):1–18Google Scholar
  19. 19.
    Tu Y, Lin Y, Wang J et al (2018) Semi-supervised learning with generative adversarial networks on digital signal modulation classification. CMC-Comput Mater Continua 55(2):243–254Google Scholar
  20. 20.
    Zhang M, Diao M, Guo L (2017) Convolutional neural networks for automatic cognitive radio waveform recognition. IEEE Access: 11074–11082CrossRefGoogle Scholar
  21. 21.
    Lin Y, Wang C, Ma C et al (2016) A new combination method for multisensor conflict information. J Supercomput 72(7):2874–2890CrossRefGoogle Scholar
  22. 22.
    Wang Y, Li Y (2012) A grid fundamental and harmonic components detection method for single-phase systems. 2012 IEEE Energy Conversion Congress and Exposition (ECCE): 4738–4745Google Scholar
  23. 23.
    Ding G, Wu Q, Yao Y-D et al (2013) Kernel-based learning for statistical signal processing in cognitive radio networks: theoretical foundations, example applications, and future directions: 133–135, IEEEGoogle Scholar
  24. 24.
    Huang JT, Li J, Yu D, Deng L, Gong Y (2013) Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers. Acoustics, speech and signal processing (ICASSP), 2013 IEEE international conference on. pp. 7304–7308. IEEEGoogle Scholar
  25. 25.
    Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural networks?. Adv Neural Inf Proces Syst: 3320–3328Google Scholar
  26. 26.
    Gonzalez RC, Woods RE, Eddins SL (2014) Digital image processing using MATLAB second edition: 273–279Google Scholar
  27. 27.
    Liu S, M L, Liu G et al (2017) A novel distance metric: generalized relative entropy [J]. Entropy 19(6):269–269CrossRefGoogle Scholar
  28. 28.
    S Liu WF, He* L et al (2017) Distribution of primary additional errors in fractal encoding method [J]. Multimed Tools Appl 76(4):5787–5802CrossRefGoogle Scholar
  29. 29.
    Palushani E, Hansen Mulvad HC, Galili M et al (2012) OTDM-to-WDM conversion based on time-to-frequency mapping by time-domain optical Fourier transformation. IEEE J Select Topics Quantum Electron 18(2):681–688CrossRefGoogle Scholar
  30. 30.
    Liu S, Z Pan WF, Cheng* X (2017) Fractal generation method based on asymptote family of generalized Mandelbrot set and its application [J]. J Nonlinear Sci Appl 10(3):1148–1161MathSciNetCrossRefGoogle Scholar
  31. 31.
    Tsai Y-R, Huang H-Y, Chen Y-C et al (2013) Simultaneous multiple carrier frequency offsets estimation for coordinated multi-point transmission in OFDM systems. IEEE Trans Wirel Commun 12(9):4558–4568CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Information and Communication EngineeringHarbin Engineering UniversityHarbinChina

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