Advertisement

Non-uniformity Detection Method Based on Space-Time Autoregressive

Conference paper
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)

Abstract

The inhomogeneous phenomena of nonhomogeneity of clutter power, interference target and isolated interference are always coexisting in the real environment of airborne radar. Therefore research on new inhomogeneous detection methods applied to the case of coexisting several inhomogeneous phenomena has become an important subject in the field of research on radar signal detection technology. The new combined space-time autoregressive (STAR) algorithm is proposed for suppressing all three kinds of inhomogeneous phenomena, while the existing STAR algorithms have no capacity, and the proposed algorithm can suppress all three kinds of inhomogeneous phenomena effectively that is indicated in the results of simulation. The simulation results show the effectiveness of the proposed algorithm.

Keywords

Radar Inhomogeneous environment Space-time autoregressive (STAR) Pulse-dimensional data 

References

  1. 1.
    Roman, J. R., Ranga, M., Davis, D. W., et al. (2000). Parametric adaptive matched filter for airborne radar applications. IEEE Transactions on Aerospace and Electronic Systems, 36(2), 677–692.CrossRefGoogle Scholar
  2. 2.
    Parker, P., & Swindlehurst, A. (2003). Space-time autoregressive filtering for matched subspace. IEEE Transactions on Aerospace and Electronic Systems, 4(2), 510–520.CrossRefGoogle Scholar
  3. 3.
    Michels, J. H. (1995). Multichannel signal detection involving temporal cross-channel correlation. IEEE Transactions on Aerospace and Electronic Systems, 10(3), 866–880.CrossRefGoogle Scholar
  4. 4.
    Roman, J. R., Rangaswamy, M., Davis, D. W., et al. (2000). Parametric adaptive matched filter for airborne radar. IEEE Transactions on Aerospace and Electronic Systems, 36(2), 677–692.CrossRefGoogle Scholar
  5. 5.
    Swindlehurst, A. L., & Parker, P. (2000). Parametric clutter rejection for space-time adaptive processing. In Proceedings of the ASAP Workshop. Lexington: MIT Lincoln Lab.Google Scholar
  6. 6.
    Lu, H., Li, Y., Chen, M., Kim, H., & Serikawa, S. (2018). Brain intelligence: Go beyond artificial intelligence. Mobile Networks and Applications, 23(2), 368–375.CrossRefGoogle Scholar
  7. 7.
    Li, Y., Lu, H., Li, J., Li, X., Li, Y., & Seiichi, S. (2016). Underwater image de-scattering and classification by deep neural network. Computers and Electrical Engineering, 54, 68–77.CrossRefGoogle Scholar
  8. 8.
    Li, Y., Lu, H., Li, K.-C., Kim, H., & Serikawa, S. (2017). Non-uniform de-scattering and de-blurring of underwater images. Mobile Networks and Applications, 23(2), 352–362.CrossRefGoogle Scholar
  9. 9.
    Deng, L., Zhu, H., Zhou, Q., & Li, Y. (2018). Adaptive top-hat filter based on quantum genetic algorithm for infrared small target detection. Multimedia Tools and Applications, 77(9), 10539–10551.CrossRefGoogle Scholar
  10. 10.
    Deng, L., & Zhu, H. (2016). Infrared moving point target detection based on spatial-temporal local contrast filter. Infrared Physics & Technology, 76, 168–173.CrossRefGoogle Scholar
  11. 11.
    Deng, L., & Zhu, H. (2015). Moving point target detection based on clutter suppression using spatial temporal local increment coding. Electronics Letters, 51(8), 625–626.CrossRefGoogle Scholar
  12. 12.
    Wu, D., Zhu, D., Shen, M., & Zhu, Z. (2012). Time-varying space-time autoregressive filtering algorithm for space-time adaptive processing. IET Radar, Sonar and Navigation, 4(6), 213–221.CrossRefGoogle Scholar
  13. 13.
    Shen, M. (2008). Research on moving target detection technology for heteroscedastic beam space-time processing. Nanjing University of Aeronautics and Astronautics Doctoral Dissertation.Google Scholar
  14. 14.
    Russ, J. A., Casbeer, D. W., & Swindlehurst, A. L. (2004). STAP detection using space-time autoregressive filtering. In Proceedings of the 2004 IEEE Radar Conference (IEEE Cat. No. 04CH37509) (pp. 541–545). IEEE.Google Scholar
  15. 15.
    Wu, B. (2007). Research on STAP technology of phased array airborne radar in heterogeneous clutter environment. National University of Defense Technology Doctoral Dissertation.Google Scholar
  16. 16.
    Zhu, H., & Deng, L. (2015). Deconvolution methods based on φHL regularization for spectral recovery. Applied Optics, 4(14), 4337–4344.CrossRefGoogle Scholar
  17. 17.
    Zhu, H. (2015). Spectral restoration using semi-blind deconvolution method with detail-preserving regularization. Infrared Physics & Technology, 69, 206–210.CrossRefGoogle Scholar
  18. 18.
    Zhu, H., Zhang, T., Yan, L., & Deng, L. (2012). Robust and fast Hausdorff distance for image matching. Optical Engineering, 51(1), 017203-1–017203-5.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ying Lu
    • 1
  1. 1.China Academy of Launch Vehicle TechnologyBeijingChina

Personalised recommendations