Cluster Computing

, Volume 22, Supplement 6, pp 14367–14375 | Cite as

Smeared spectrum jamming suppression based on time unit analysis and polarization cancellation

  • Xin LiEmail author
  • Chunyang Wang
  • Hui Yuan
  • Junpeng Shi


By analyzing the time structure after pulse compression, an electronic counter-countermeasure (ECCM) combining time unit analysis and polarization cancellation is proposed in this paper to counter smeared spectrum (SMSP) jamming, which is an effective jamming type to linear frequency modulation (LFM) signals. The stretch processing to receive signals in main and auxiliary channels is firstly done and the time structure differences of pulse compression results are analyzed. Based on the time structure differences, the main-to-auxiliary ratio is defined, and the characteristics of this function in different time units are analyzed. According to the statistical parameter of modulus and phase ratio in different time units, the jamming suppression factor is computed by searching the jamming-only time unit. The influences of background noise and polarization purity are analyzed finally. Some computer simulation results are given to prove the effectiveness of the proposed method.


Smeared spectrum (SMSP) jamming Linear frequency modulation (LFM) signal Time units analysis Polarization cancellation 



This work was supported by the National Science Foundation of China (Grant No. 61601502).


  1. 1.
    Gong, S.X., Wei, X.Z., Li, X.: ECCM scheme against interrupted sampling repeater jammer based on time-frequency analysis. J. Syst. Eng. Electron. 25(6), 996–1003 (2014)CrossRefGoogle Scholar
  2. 2.
    Schleher, D.C.: Electronic warfare in the information age. American Artech House (2000)Google Scholar
  3. 3.
    Waston, C.J.: Comparison of DDS and DRFM Techniques in the Generation of “Smart Noise” Jamming Waveforms. Naval Postgraduate School, California (1996)Google Scholar
  4. 4.
    Camp, W.W., Mayhan, J.T., Donnell, R.M.O.: Wideband radar for ballistic missile defense and range-doppler imaging of satellites. Lincoln Lab. J. 12(2), 267–280 (2000)Google Scholar
  5. 5.
    Sparrow, M.J., Cakilo, J.: ECM techniques to counter pulse compression radar, United States Patent, 7081846 (2006)Google Scholar
  6. 6.
    Li, Y.P., Ying, X., Tang, B.: SMSP jamming identification based on Matched Signal Transform. In: International Conference on Computational Problem-Solving, pp. 182–185 (2011)Google Scholar
  7. 7.
    Bu, T.X., Dong, Y., Wu, G.X., et al.: A study on technology of recognizing SMSP jamming. Mod. Radar 37(3), 42–45 (2015). (in Chinese) Google Scholar
  8. 8.
    Yin, H.W., Li, G.L., Lu, C.H.: An algorithm of deception jamming suppression based on complex-value blind source separation. J. Shanghai Jiao Tong Univ. 49(10), 1564–1569 (2015). (in Chinese) Google Scholar
  9. 9.
    Novey, M., Adall, T.: Complex ICA by negentropy maximization. IEEE Trans. Neural Netw. 19(4), 596–609 (2008)CrossRefGoogle Scholar
  10. 10.
    Li, F., Li, G.L., Nian, P.L.: Radar signal deception jamming suppressing based on blind Source separation. J. Naval Aeronaut. Astronaut. Univ. 30(5), 424–428 (2015). (in Chinese) Google Scholar
  11. 11.
    Sun, M.H., Tang, B.: Suppression of smeared spectrum ECM signal. J. Chin. Inst. Eng. 32(3), 407–413 (2009)CrossRefGoogle Scholar
  12. 12.
    Sun, M.H.: Research on radar deception jamming suppression algorithm. In: Chengdu: University of Electronic Science and Technology of China (2008) (in Chinese)Google Scholar
  13. 13.
    Lu, Y.L., Li, M., Cao, R.Q., et al.: Jointing time-frequency distribution and compressed sensing for countering smeared spectrum jamming. J. Electron. Info. Technol. 38(12), 3275–3281 (2016). (in Chinese) Google Scholar
  14. 14.
    Sournekh, M.: SAR-ECCM using phase-perturbed LFM chirp signals and DRFM repeat jammer penalization. IEEE Trans. Aerosp. Electron. Syst. 42(1), 191–205 (2006)CrossRefGoogle Scholar
  15. 15.
    Akhtar, J.: Orthogonal block coded ECCM schemes against repeat radar jammers. IEEE Trans. Aerosp. Electron. Syst. 45(3), 1218–1226 (2009)CrossRefGoogle Scholar
  16. 16.
    Shi, L.F., Wang, X.S., Xiao, S.P.: Polarization Discrimination Between Repeater False Target and Radar Target. Sci. China Ser. F 52(1), 149–158 (2009)zbMATHGoogle Scholar
  17. 17.
    Shi, L.F., Wang, X.S., Xu, Z.H., et al.: The iterative-filtering scheme and its performance analysis of APC. J. Electron. Info. Technol. 28(9), 1560–1564 (2006). (in Chinese) Google Scholar
  18. 18.
    Stapor, D.P.: Optimal receive antenna polarization in the presence of interference and noise. IEEE Trans Antenna Propag. 43(5), 473–477 (1995)CrossRefGoogle Scholar
  19. 19.
    Wang, X.S., Wang, L.D., Xiao, S.P., et al.: Theoretical performance analysis of adaptive polarization filters. Acta Electron. Sinica 32(8), 1326–1329 (2004). (in Chinese) Google Scholar
  20. 20.
    Wang, X.S., Chang, Y.L., Dai, D.H., et al.: Band characteristics of sinr polarization filter. IEEE Trans Antenna Propag. 55(4), 1148–1154 (2007)CrossRefGoogle Scholar
  21. 21.
    Shi, L.F., Wang, X.S., Xu, Z.H., et al.: The iterative filtering scheme and its performance analysis of APC. J. Electron. Info. Technol. 28(9), 1560–1564 (2006). (in Chinese) Google Scholar
  22. 22.
    Ren, B., Luo, X.B., Deng, F.G., et al.: Design of fast adaptive polarization filters utilizing polarizing cluster center. J. Nat. Univ. Defense Technol. 37(4), 87–92 (2015). (in Chinese) Google Scholar
  23. 23.
    Ren, B., Shi, L.F., Wang, G.Y.: Study on the performance of interference suppression polarization filter based on environment disturbance model. Acta Electron. Sinica 44(3), 1326–1329 (2016). (in Chinese) Google Scholar
  24. 24.
    Luo, J., Wang, X.S., Li, Y.Z., et al.: Description and analysis of spacial polarization characteristics of antenna. Chin. J. Radio Sci. 23(4), 620–628 (2008). (in Chinese) Google Scholar
  25. 25.
    Liu, Y., Dai, H.Y., Li, J.L., et al.: Principle and experimental results of spatial virtual polarization filtering algorithm[J]. Chin. J. Radio Sci. 26(2), 620–628 (2011). (in Chinese) Google Scholar
  26. 26.
    Luo, C., Wang, J.: Fine-grained representation learning in convolutional autoencoders[J]. J. Electron. Imaging 25(2), 1–12 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Air and Missile Defense CollegeAir Force Engineering UniversityXi’anChina

Personalised recommendations