Skip to main content
Log in

Deep Learning-Based Approach for Low Probability of Intercept Radar Signal Detection and Classification

  • THEORY AND METHODS OF SIGNAL PROCESSING
  • Published:
Journal of Communications Technology and Electronics Aims and scope Submit manuscript

Abstract

Detection and classification of Low Probability of Interception (LPI) radar signals is one of the most important challenges in electronic warfare (EW), since there are limited methods for identifying these type of signals. In this paper, a radar waveform automatic identification system for detecting and classifying LPI radar is studied, and accordingly we propose a method based on deep learning networks to detect and classify LPI radar waveforms. To this end, the GoogLeNet architecture as one of the well-known convolutional neural networks (CNN) is utilized. We employ the Short Time Fourier Transform (STFT) for time-frequency analysis in order to construct the entry image for proposed method 1,2 (improved the GoogLeNet and AlexNet networks) to recognize offline training and online recognition. After the training procedure with the supervised data sets the proposed method 1,2 can detect and classify nine modulation types of LPI radar, including LFM, poly-phase (P1, P2, P3, P4) and poly-time (T1, T2, T3, T4) waveforms. The numerical results for proposed method 1, show considerable accuracies up to 98.7% at the SNR level of –15 dB, which outperforms the existing methods.

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

Access this article

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.
Fig. 8.
Fig. 9.
Fig. 10.

Similar content being viewed by others

Notes

  1. The ReLU is the most commonly used activation function in deep learning models. The function returns 0 if it receives any negative input, but for any positive value \(x\) it returns back the input value, i.e.: f(x) = max(0,\(x\)).

REFERENCES

  1. P. E. Pace, Detecting and Classifying Low Probability of Intercept Radar (Artech House, Norwood, 2009).

    Google Scholar 

  2. Seung-Hyun Kong, Minjun Kim, Linh Manh Hoang, and Eunhui Kim, “Automatic LPI radar waveform recognition using CNN,” IEEE Access 6, 4207−4219 (2018).

    Article  Google Scholar 

  3. Chao Wang, Hao Gao, and Xu-Dong Zhang, “Radar signal classification based on auto-correlation function and directed graphical model,” in Proc. 6th Int. Conf. on Signal Process., Commun. & Comput. (ICSPCC), Hong Kong, August, 2016 (ICSPCC, 2016).

  4. K. Konopko, Y. P. Grishin, and D. Jańczak, “Radar signal recognition based on time-frequency representations and multidimensional probability density function estimator,” Signal Processing Symp. (SPSympo), 1−6 (2015).

  5. R. Mingqiu, C. Jinyan, and Z. Yuanqing, “Classification of radar signals using time-frequency transforms and fuzzy clustering,” in Microwave and Millimeter Wave Technology (ICMMT 2010), (Int. Conf., Chengdu, China, May 8−11,2010) (ICMMT, 2010), pp. 2067−2070.

  6. D. Zeng, X. Zeng, G. Lu, and B. Tang, “Automatic modulation classification of radar signals using the generalised time-frequency representation of Zhao, Atlas and Marks,” IET Radar, Sonar & Navigation 5, 507−516 (2011).

    Article  Google Scholar 

  7. L. Cohen, “Time-frequency distributions. A review,” Proc. IEEE 77, 941−981 (1989).

    Article  Google Scholar 

  8. H. Zhang, G. Bi, W. Yang, S. G. Razul, and C. M. S. See, “IF estimation of FM signals based on time-frequency image,” IEEE Trans. Aerosp. Electron. Syst. 51, 326−343 (2015).

    Article  Google Scholar 

  9. G. J. Upperman, T. L. Upperman, D. J. Fouts, and P. E. Pace, “Efficient time-frequency and bi-frequency signal processing on a reconfigurable computer,” in Proc. 42nd Asilomar Conf. on Signals, Systems and Computers, Pacific Grove, CA, USA, Oct. 26−29 2008 (IEEE, New York. 2008).

  10. M. Zhang, M. Diao, and L. Guo, “Convolutional neural networks for automatic cognitive radio waveform recognition,” IEEE Access 5, 11074−11082 (2017).

    Article  Google Scholar 

  11. G. López-Risueño, J. Grajal, and A. Sanz-Osorio, “Digital channelized receiver based on time-frequency analysis for signal interception,” IEEE Trans. Aerosp. Electron. Syst. 41, 879−898 (2005).

    Article  Google Scholar 

  12. C. Wang, J. Wang, and X. Zhang, “Automatic radar waveform recognition based on time-frequency analysis and convolutional neural network,” in Proc. IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP 2017), Hilton New Orleans Riverside, Mar. 2017 (IEEE, New York. 2017), pp. 2437−2441.

    Google Scholar 

  13. F. Hejazikookamari, M. M. Nayebi, Y. Norouzi, and E. S. Kashani, “A novel method to detect and localize LPI radars,” IEEE Trans. Aerosp. Electron. Syst. 55, 2327–2336 (2019).

    Article  Google Scholar 

  14. M. Zhang, L. Liu, and M. Diao, “LPI radar waveform recognition based on time-frequency distribution,” Sensors 16, 1682 (2016).

    Article  Google Scholar 

  15. Y. LeCun, P. Haffner, L. Bottou, and Y. Bengio, “Object recognition with gradient-based learning,” Shape, Contour and Grouping in Computer Vision, 319−345 (1999).

  16. Y. LeCun and Y. Bengio, “Convolutional networks for images, speech, and time series,” in The Handbook of Brain Theory and Neural Networks (MIT Press, Cambridge, MA, 1995).

    Google Scholar 

  17. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Computer Vision and Pattern Recognition, IEEE Computer Soc. Conf.2009 (CVPR Workshops, 2009) (2 Vols).

  18. L. Guo and X. Chen, “Low probability of intercept radar signal recognition based on the improved AlexNet model,” in Proc. 2nd Int. Conf. on Digital Signal Processing, (ICDSP), Japan, Tokyo, Feb. 25−27,2018 (ICDSP, 2018), pp. 119–124.

  19. Ch. Szegedy, Wei Liu, Yangqing Jia, P. Sermanet, S. E. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich “Going deeper with convolutions,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, June 7−12,2015 (IEEE, New York, 2015), pp. 7−12.

  20. A. Krizhevsky, I. Sutskever, G. E. Hinton, “Gradient-based learning applied to document recognation,” Commun. ACM 60, 84−90 (2017).

    Article  Google Scholar 

  21. M. I. Skolnik, “Introduction to radar,” in Radar Handbook, Vol. 2 (McGraw-Hill, New York, NY, 1962).

    Google Scholar 

  22. N. Levanon and E. Mozeson, Radar Signals (Wiley, New York, NY, 2004).

    Book  Google Scholar 

  23. E. Sejdić, I. Djurović, and J. Jiang, “Time-frequency feature representation using energy concentration: An overview of recent advances,” Digital Signal Process. 19, 153−183 (2009).

    Article  Google Scholar 

  24. K. Konopko, “A detection algorithm of Lpi radar signals,” in Proc. IEEE Conf. Signal Processing Algorithms, Architectures, Arrangements, and Applications (SPA), Poznan, Poland, Sept. 20−22, 2007 (IEEE, New York, 2007).

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to R. Bayderkhani.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ghadimi, G., Norouzi, Y., Bayderkhani, R. et al. Deep Learning-Based Approach for Low Probability of Intercept Radar Signal Detection and Classification. J. Commun. Technol. Electron. 65, 1179–1191 (2020). https://doi.org/10.1134/S1064226920100034

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1064226920100034

Keywords:

Navigation