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Classification of UAV-to-ground vehicles based on micro-Doppler effect and bispectrum analysis

  • Lingzhi Zhu
  • Shuning ZhangEmail author
  • Si Chen
  • Huichang Zhao
  • Xiangyu Lu
  • Dongxu Wei
Original Paper
  • 38 Downloads

Abstract

Vehicles such as armored cars and tanks have a big threat due to their flexibility and lethality in modern wars. In order to destroy them without casualties, the unmanned aerial vehicles (UAVs) are widely used in local high-precision strike. For the purpose of best attack plan, it is necessary and significant to find a way that can distinguish ground wheeled vehicles and ground tracked vehicles from the UAV with high accuracy. In this paper, a classification method based on micro-Doppler effect and bispectrum analysis is proposed. Firstly, models describing relationship between ground vehicles and the UAV are established to derive radar echo signals. Secondly, bispectrum is utilized to analyze echo signals and diagonal slice of the bispectrum is obtained by calculating the third-order accumulation of echo signal. According to the difference of ground vehicles, three features are extracted. Thirdly, these features are sent to support vector machine for classification. Results using simulated data and measured data in different cases prove the effectiveness and robustness of proposed method. Comparison with current methods also verifies the superiority of method in this paper.

Keywords

Classification UAV-to-ground vehicles Micro-Doppler Bispectrum analysis SVM 

Notes

Acknowledgements

This research was partially supported by Natural Science Foundation of Jiangsu Province (BK20160848), National Natural Science Foundation of China (NSFC) (61801220) and Fundamental Research Funds for the Central Universities (30917011315).

References

  1. 1.
    Fetz, V., Prochnow, H., Brönstrup, M.: Target identification by image analysis. Nat. Product Rep. 33(5), 655–667 (2016)CrossRefGoogle Scholar
  2. 2.
    Huan, R.H., Pan, Y.: Target recognition for multi - aspect SAR images with fusion strategies. Progres. Electromagn. Res. 134(134), 267–288 (2013)CrossRefGoogle Scholar
  3. 3.
    Dong, W.G., Yan-Jun, L.I.: Radar target recognition based on micro-Doppler effect. Optoelectron. Lett. 4(6), 456–459 (2008)CrossRefGoogle Scholar
  4. 4.
    Chen, V.C.: Micro-Doppler effect in radar: phenomenon, model, and simulation study. IEEE Trans. Aerosp. Electron. Syst. 42(1), 2–21 (2006)CrossRefGoogle Scholar
  5. 5.
    Nanzer, J. A., Chen, V. C.: Microwave interferometric and Doppler radar measurements of an UAV. In: Radar Conference. IEEE. 1628–1633 (2017)Google Scholar
  6. 6.
    Li, P., Wang, D.C., Wang, L.: Separation of micro-Doppler signals based on time frequency filter and Viterbi algorithm. Signal Image Video Process. 7(3), 593–605 (2013)CrossRefGoogle Scholar
  7. 7.
    Li, P., Wang, D.C., Chen, J.L.: Parameter estimation for micro-Doppler signals based on cubic phase function. Signal Image Video Process. 7(6), 1239–1249 (2013)CrossRefGoogle Scholar
  8. 8.
    Kim, Y., Moon, T.: Human detection and activity classification based on micro-Doppler signatures using deep convolutional neural networks. IEEE Geosci. Remote Sens. Lett. 13(1), 8–12 (2016)CrossRefGoogle Scholar
  9. 9.
    Fioranelli, F., Ritchie, M., Griffiths, H.: Classification of unarmed/armed personnel using the NetRAD multistatic radar for micro-Doppler and singular value decomposition features. IEEE Geosci. Remote Sens. Lett. 12(9), 1933–1937 (2015)CrossRefGoogle Scholar
  10. 10.
    Ritchie, M., Fioranelli, F., Borrion, H.: Multistatic micro-Doppler radar feature extraction for classification of unloaded/loaded micro-drones. IET Radar Sonar Navig. 11(1), 116–124 (2017)CrossRefGoogle Scholar
  11. 11.
    Jian, H.: Micro-Doppler features based parameter estimation and identification of tank. J. Electron. Inform. Technol. 32(5), 1050–1055 (2010)Google Scholar
  12. 12.
    Stove, A.G.: A Doppler-based target classifier using linear discriminants and principal components. In: IET Seminar on High Resolution Imaging and Target Classification, pp. 107–125 (2007)Google Scholar
  13. 13.
    Chen, F., Liu, H.W., Du, L.: Target classification with low-resolution radar based on dispersion situations of eigenvalue spectra. Sci. China Inform. Sci. 53(7), 1446–1460 (2010)CrossRefGoogle Scholar
  14. 14.
    Li, Y.-B., Du, L., Liu, H.W., Wang, B.-S.: Study on classification of wheeled and tracked vehicles based on micro-Doppler effect and multilevel wavelet decomposition. J. Electron. Inform. Technol. 35(4), 894–900 (2013)CrossRefGoogle Scholar
  15. 15.
    Li, Y., Du, L., Liu, H.: Hierarchical classification of moving vehicles based on empirical mode decomposition of micro-Doppler signatures. IEEE Trans. Geosci. Remote Sens. 51(5), 3001–3013 (2013)CrossRefGoogle Scholar
  16. 16.
    Scoccimarro, R., Feldman, H.A., Fry, J.N.: The bispectrum of IRAS redshift catalogs. Astrophys. J. 546(2), 652–664 (2002)CrossRefGoogle Scholar
  17. 17.
    Nikias, C.L., Raghuveer, M.R.: Bispectrum estimation: a digital signal processing framework. Proc. IEEE 75(7), 869–891 (1987)CrossRefGoogle Scholar
  18. 18.
    Liu, C., Zhang, Z., Dou, R.: Radar target recognition based on time-domain bispectra feature. J. Data Acquis. Process. 24(6), 709–713 (2009)Google Scholar
  19. 19.
    Ya-Jun, L.I., Yue, L.I., Gao, Y.: A method of extracting seismic wavelet based on bispectrum amplitude and phase reconstruction. Prog. Geophys. 22(3), 947–952 (2007)Google Scholar
  20. 20.
    Xing, W., Zhou, Y., Zhou, D.: Research on low probability of intercept radar signal recognition using deep belief network and bispectra diagonal slice. J. Electron. Inform. Technol. 38(11), 2792–2796 (2016)Google Scholar
  21. 21.
    Jiang, L., Yawen, J.I., Yang, T.: Ultra-wideband radar human target recognition based on bispectrum feature. Telecommun. Eng. 55(9), 953–958 (2015)Google Scholar
  22. 22.
    Chen, S., Yang, X.: Alternative linear discriminant classifier. Pattern Recognit. 37(7), 1545–1547 (2004)CrossRefzbMATHGoogle Scholar
  23. 23.
    Yu, F., Xu, X.: A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network. Appl. Energy 134(134), 102–113 (2014)CrossRefGoogle Scholar
  24. 24.
    Zou, Q., Ju, Y., Li, D.: Protein folds prediction with hierarchical structured SVM. Curr. Proteom. 13(2), 79–85 (2016)CrossRefGoogle Scholar
  25. 25.
    Keerthi, S.S., Shevade, S.K., Bhattacharyya, C.: Improvements to Platt’s SMO algorithm for SVM classifier design. Neural Comput. 13(3), 637–649 (2014)CrossRefzbMATHGoogle Scholar
  26. 26.
    Dai. Y., Zhang. H., Sun. X.: Extraction of micro-Doppler signal based on the combination of CLEAN and L-statistics method. Geoscience & Remote Sensing Symposium IEEE. 2707–2710 (2016)Google Scholar
  27. 27.
    Zhenhui Shen.: Study on detection and parameter estimation of DS-SS/BPSK signal with low signal-to-noise ratio. University of Electronic Science and Technology of China (2003)Google Scholar
  28. 28.
    Liang, J., Shi, Z.: Li D (2006) Information entropy, rough entropy and knowledge granulation in incomplete information systems. Int. J. Gen Syst 35(6), 641–654 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Zhu, L.Z., Chen, S., Zhao, H.C., Zhang, S.N.: Classification of UAV-to-ground vehicles based on micro-Doppler signatures using singular value decomposition and reconstruction. Optik 181, 598–610 (2019)CrossRefGoogle Scholar
  30. 30.
    Zhu, L.Z., Zhang, S.N., Zhao, H.C., Chen, S.: Classification of UAV-to-ground vehicles based on micro-Doppler signatures using singular value decomposition and deep convolutional neural networks. IEEE Access. 7, 22133–22143 (2019)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Lingzhi Zhu
    • 1
  • Shuning Zhang
    • 1
    Email author
  • Si Chen
    • 1
  • Huichang Zhao
    • 1
  • Xiangyu Lu
    • 1
  • Dongxu Wei
    • 1
  1. 1.School of Electronic and Optical EngineeringNanjing University of Science and TechnologyNanjingChina

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