Skip to main content
SpringerLink
Log in

Automatic modulation recognition of radar signals based on histogram of oriented gradient via improved principal component analysis

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

Abstract

Automatic modulation recognition (AMR) of radar signals plays a critical role in electronic reconnaissance. Current AMR algorithms are mainly based on convolutional neural networks (CNN), which can learn the feature hierarchy by establishing high-level features from low-level features. However, for time–frequency analysis-based methods, distinct low-level features in the time–frequency spectrum can already reflect modulation characteristics. Thus, this study develops a novel approach based on low-level shape descriptors via histograms of oriented gradients (HOG) and support vector machine (SVM). Comparison studies with classic CNN-based methods have also been done to reveal the superiority of the designed approach. Experimental results demonstrate that the HOG-SVM approach has a more efficient performance. To further enhance the classification precision under low signal-to-noise ratios, an improved principal component analysis denoising algorithm is developed to improve signal quality under intense noise background. Experiments based on simulated and measured signals demonstrate that the proposed algorithm can accurately distinguish signals under intense noise environments.

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
Fig. 8
Fig. 9

Similar content being viewed by others

Availability of data and materials

Not applicable.

References

  1. Gupta, M., Hareesh, G., Mahla, A.K.: Electronic warfare: issues and challenges for emitter classification. Defence Sci. J. 61(3), 228–234 (2011)

    Article  Google Scholar 

  2. Avci, D.: An intelligent system using adaptive wavelet entropy for automatic analog modulation identification. Digit. Signal Process. 20(4), 1196–1206 (2010)

    Article  Google Scholar 

  3. Zhang, M., Liu, L., Diao, M.: LPI radar waveform recognition based on time-frequency distribution. Sensors 16(10), 1682 (2016)

    Article  Google Scholar 

  4. Qu, Z., Mao, X., Hou, C.: Radar signal recognition based on singular value entropy and fractal dimension. Syst. Eng. Electron. 40(2), 303–307 (2018)

    Google Scholar 

  5. Lunden, J., Koivunen, V.: Automatic radar waveform recognition. IEEE J. Select. Top. Signal Process. 1(1), 124–136 (2007)

    Article  Google Scholar 

  6. Sahraeian, R., Van Compernolle, D.: Crosslingual and multilingual speech recognition based on the speech manifold. IEEE/ACM Trans. Audio Speech Lang. Process. 25(12), 2301–2312 (2017)

    Article  Google Scholar 

  7. Sarikaya, R., Hinton, G.E., Deoras, A.: Application of deep belief networks for natural language understanding. IEEE/ACM Trans. Audio Speech Lang. Process. 22(4), 778–784 (2014)

    Article  Google Scholar 

  8. Fan, J., et al.: HD-MTL: hierarchical deep multi-task learning for large-scale visual recognition. IEEE Trans. Image Process. 26(4), 1923–1938 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  9. Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition (2015). arXiv:1409.1556

  10. Wang, Y., Liu, M., Yang, J., Gui, G.: Data-driven deep learning for automatic modulation recognition in cognitive radios. IEEE Trans. Veh. Technol. 68(4), 4074–4077 (2019)

    Article  Google Scholar 

  11. Li, R., Li, L., Yang, S., Li, S.: Robust automated VHF modulation recognition based on deep convolutional neural networks. IEEE Commun. Lett. 22(5), 946–949 (2018)

    Article  Google Scholar 

  12. Wei, S., Qu, Q., Su, H., Wang, M., Shi, J., Hao, X.: Intra-pulse modulation radar signal recognition based on CLDN network. IET Radar Sonar Navig. 14(6), 803–810 (2020)

    Article  Google Scholar 

  13. Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  14. Zhang, M., Diao, M., Guo, L.: Convolutional neural networks for automatic cognitive radio waveform recognition. IEEE Access. 5, 11074–11082 (2017)

    Article  Google Scholar 

  15. Qu, Z., Mao, X., Deng, Z.: Radar signal intra-pulse modulation recognition based on convolutional neural network. IEEE Access 6, 43874–43884 (2018)

    Article  Google Scholar 

  16. Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)

    Article  Google Scholar 

  17. Qu, Z., Hou, C., Hou, C., Wang, W.: Radar signal intra-pulse modulation recognition based on convolutional neural network and deep Q-learning network. IEEE Access 8, 49125–49136 (2020)

    Article  Google Scholar 

  18. Han, H., Ren, Z., Li, L., Zhu, Z.: Automatic modulation classification based on deep feature fusion for high noise level and large dynamic input. Sensors 21(6), 2117 (2021)

    Article  Google Scholar 

  19. Wang, Q., Du, P., Liu, X.: Adversarial Unsupervised domain adaptation for cross scenario waveform recognition. Signal Process. 171, 107526 (2020)

    Article  Google Scholar 

  20. Jolliffe, I.T.: Principal Component Analysis. Springer, Berlin (1986)

    Book  MATH  Google Scholar 

  21. Du, L., Wang, B., Wang, P., Ma, Y., Liu, H.: Noise reduction method based on principal component analysis with beta process for micro-doppler radar signatures. IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 8(8), 4028–4040 (2015)

    Article  Google Scholar 

  22. Zhao, X., Ye, B.: Similarity of signal processing effect between Hankel matrix-based SVD and wavelet transform and its mechanism analysis. Mech. Syst. Signal Process 23, 1062–1075 (2009)

    Article  Google Scholar 

  23. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)

    MATH  Google Scholar 

  24. Tipping, M.E., Bishop, C.M.: Probabilistic principle component analysis. J. Roy. Stat. Soc. B6(13), 611–622 (1999)

    Article  MATH  Google Scholar 

  25. Nyamundanda, G., Brennan, L., Gormley, I.C.: Probabilistic principal component analysis for metabolomic data. BMC Bioinf. 11, 571–581 (2010)

    Article  Google Scholar 

  26. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), San Diego, CA, USA, pp. 886–893 (2005)

  27. Pang, Y., Yuan, Y., Li, X.: Efficient HOG human detection. Signal Process. 91(4), 773–781 (2011)

    Article  MATH  Google Scholar 

  28. Li, B., Cheng, L., Yu, Z.: Histogram of oriented gradient based gist feature for building recognition. Comput. Intell. Neurosci. 10, 1–9 (2016)

    Google Scholar 

  29. Prates, R., Cámara-Chávez, G., Schwartz, W.R., et al.: Brazilian license plate detection using histogram of oriented gradients and sliding windows. Int. J. Comput. Sci. Inform. Technol. 5(6), 39–52 (2013)

    Google Scholar 

  30. Zhang, X., An, F., et al.: A hardware-oriented histogram of oriented gradients algorithm and its VLSI implementation. Jpn. J. Appl. Phys. 56(4156), 0401 (2017)

    Article  Google Scholar 

  31. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  MathSciNet  Google Scholar 

  32. Aslan, M.F., Durdu, A., Sabanci, K., et al.: CNN and HOG based comparison study for complete occlusion handling in human tracking. Measurement 158, 107704 (2020)

    Article  Google Scholar 

  33. Pasadas, D.J., Baskaran, P., Ramos, H.G., Ribeiro, A.L.: Detection and classification of defects using ECT and multi-level SVM model. IEEE Sens. J. 20(5), 2329–2338 (2020)

    Article  Google Scholar 

Download references

Funding

This research was financially supported by National Natural Science Foundation of China (61971226, 61801220) and Natural Science Foundation of Jiangsu Province for Excellent Young Scholars (BK20200075).

Author information

Authors and Affiliations

  1. School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Kuiyu Chen, Si Chen, Shuning Zhang & Huichang Zhao

Authors
  1. Kuiyu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Si Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shuning Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Huichang Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Kuiyu Chen contributed to software and writing; others performed reviewing and editing.

Corresponding author

Correspondence to Kuiyu Chen.

Ethics declarations

Conflict of interest

The authors declared that they have no conflicts of interest to this work.

Ethical approval

Not applicable.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, K., Chen, S., Zhang, S. et al. Automatic modulation recognition of radar signals based on histogram of oriented gradient via improved principal component analysis. SIViP 17, 3053–3061 (2023). https://doi.org/10.1007/s11760-023-02526-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-023-02526-x

Keywords

Search

Navigation