Skip to main content
SpringerLink
Log in

Machine learning-based radar waveform classification for cognitive EW

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

In this paper, we propose a waveform classification approach for cognitive electronic warfare applications in which a supervised classification method is presented in an efficient framework. In this manner, we introduce an end-to-end framework for detection and classification of radar pulses. Our approach is complete, i.e., we provide raw radar signal at the input side and produce categorical output in the end. We use short-time Fourier transform to obtain time–frequency image (TFI) of the signal. Hough transform is used to detect pulses in TFIs. Convolutional neural networks (CNN) are used for intra-pulse modulation classification. In experiments, we provide supervised classification results at different signal-to-noise ratio (SNR) levels and achieve 98.08% classification accuracy for 10 dB SNR on a diverse set of both frequency- and phase-modulated signals. The method sustains high classification accuracy levels as [93.9%;85.83%] for 0 and \(-10\) dB SNR, respectively, that signifies the robustness of the method against noise for low-power intercepted signals. Simulation results also show that CNN outperforms artificial neural networks in intra-pulse modulation classification task.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Adamy, D.: EW 101: A First Course in Electronic Warfare. Artech House radar library. Artech House (2001). https://books.google.com.tr/books?id=ta3hngEACAAJ

  2. Adamy, D.: EW 102: A second course in electronic warfare. Artech house radar library. Artech house (2004). https://books.google.com.tr/books?id=-AkfVZskc64C

  3. Ayazgok, S., Erdem, C., Ozturk, M.T., Orduyilmaz, A., Serin, M.: Automatic antenna scan type classification for next-generation electronic warfare receivers. IET Radar Sonar Navig. 12(4), 466–474 (2018). https://doi.org/10.1049/iet-rsn.2017.0354

    Article  Google Scholar 

  4. Chao Wang, Gao, H., Xudong Zhang: Radar signal classification based on auto-correlation function and directed graphical model. In: 2016 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), pp. 1–4 (2016)

  5. He, K., Gkioxari, G., Dollár, P., Girshick, R.B.: Mask R-CNN. CoRR (2017). abs/1703.06870http://arxiv.org/abs/1703.06870

  6. Hsiung, W.C.: A prototype rule based system for electronic warfare (1991)

  7. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32Nd International Conference on International Conference on Machine Learning - Volume 37, ICML’15, pp. 448–456. JMLR.org (2015). http://dl.acm.org/citation.cfm?id=3045118.3045167

  8. Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Large-scale video classification with convolutional neural networks. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1725–1732 (2014). https://doi.org/10.1109/CVPR.2014.223

  9. Kawalec, A., Owczarek, R.: Radar emitter recognition using intrapulse data. In: 15th International Conference on Microwaves, Radar and Wireless Communications (IEEE Cat. No.04EX824), vol. 2, pp. 435–438 Vol.2 (2004). https://doi.org/10.1109/MIKON.2004.1357059

  10. Kong, S., Kim, M., Hoang, L.M., Kim, E.: Automatic lpi radar waveform recognition using cnn. IEEE Access 6, 4207–4219 (2018). https://doi.org/10.1109/ACCESS.2017.2788942

    Article  Google Scholar 

  11. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1, NIPS’12, pp. 1097–1105. Curran Associates Inc., USA (2012). http://dl.acm.org/citation.cfm?id=2999134.2999257

  12. Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998). https://doi.org/10.1109/5.726791

    Article  Google Scholar 

  13. Levanon, N., Mozeson, E.: Radar Signals (2004). https://doi.org/10.1002/0471663085

  14. Lunden, J., Koivunen, V.: Automatic radar waveform recognition. IEEE J. Sel. Topics Signal Process. 1(1), 124–136 (2007). https://doi.org/10.1109/JSTSP.2007.897055

    Article  Google Scholar 

  15. Mingqiu, R., Jinyan, C., Yuanqing, Z.: Classification of radar signals using time-frequency transforms and fuzzy clustering (2010). https://doi.org/10.1109/ICMMT.2010.5525213

  16. Mingqiu Ren, Jinyan Cai, Yuanqing Zhu, Minghao He: Radar emitter signal classification based on mutual information and fuzzy support vector machines. In: 2008 9th International Conference on Signal Processing, pp. 1641–1646 (2008). https://doi.org/10.1109/ICOSP.2008.4697451

  17. Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on International Conference on Machine Learning, ICML’10, pp. 807–814. Omnipress, USA (2010). http://dl.acm.org/citation.cfm?id=3104322.3104425

  18. Pace, P.: Detecting and Classifying Low Probability of Intercept Radar. Artech House radar library. Artech House (2009). https://books.google.com.tr/books?id=K_T4M-nA6JYC

  19. Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: H. Wallach, H. Larochelle, A. Beygelzimer, F. d’ Alché-Buc, E. Fox, R. Garnett (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035. Curran Associates, Inc. (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf

  20. Skolnik, M.: Radar Handbook, Third Edition. Electronics electrical engineering. McGraw-Hill Education (2008). https://books.google.com.tr/books?id=76uF2Xebm-gC

  21. Wang, C., Wang, J., Zhang, X.: Automatic radar waveform recognition based on time-frequency analysis and convolutional neural network. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2437–2441 (2017). https://doi.org/10.1109/ICASSP.2017.7952594

  22. Wiley, R.: ELINT: The Interception and Analysis of Radar Signals. Artech House radar library. Artech House (2006). https://books.google.com.tr/books?id=vQNTAAAAMAAJ

  23. Yar, E., Kocamis, M.B., Orduyilmaz, A., Serin, M., Efe, M.: A complete framework of radar pulse detection and modulation classification for cognitive ew. In: 2019 27th European Signal Processing Conference (EUSIPCO), pp. 1–5 (2019)

Download references

Author information

Authors and Affiliations

  1. TUBITAK BILGEM ILTAREN, 06800, Ankara, Turkey

    Adnan Orduyilmaz, Ersin Yar, Mehmet Burak Kocamis & Mahmut Serin

  2. Department of Electrical and Electronics Engineering, Ankara University, 06560, Ankara, Turkey

    Murat Efe

Authors
  1. Adnan Orduyilmaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ersin Yar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mehmet Burak Kocamis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mahmut Serin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Murat Efe
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Adnan Orduyilmaz.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Orduyilmaz, A., Yar, E., Kocamis, M.B. et al. Machine learning-based radar waveform classification for cognitive EW. SIViP 15, 1653–1662 (2021). https://doi.org/10.1007/s11760-021-01901-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-021-01901-w

Keywords

Search

Navigation