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Non-uniformity Detection Method Based on Space-Time Autoregressive

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2nd EAI International Conference on Robotic Sensor Networks

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

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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.

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References

  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.

    Article  Google Scholar 

  2. Parker, P., & Swindlehurst, A. (2003). Space-time autoregressive filtering for matched subspace. IEEE Transactions on Aerospace and Electronic Systems, 4(2), 510–520.

    Article  Google Scholar 

  3. Michels, J. H. (1995). Multichannel signal detection involving temporal cross-channel correlation. IEEE Transactions on Aerospace and Electronic Systems, 10(3), 866–880.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  10. Deng, L., & Zhu, H. (2016). Infrared moving point target detection based on spatial-temporal local contrast filter. Infrared Physics & Technology, 76, 168–173.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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. 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. Zhu, H., & Deng, L. (2015). Deconvolution methods based on φHL regularization for spectral recovery. Applied Optics, 4(14), 4337–4344.

    Article  Google Scholar 

  17. Zhu, H. (2015). Spectral restoration using semi-blind deconvolution method with detail-preserving regularization. Infrared Physics & Technology, 69, 206–210.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. China Academy of Launch Vehicle Technology, Beijing, China

    Ying Lu

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  1. Ying Lu
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Kitakyushu, Japan

    Huimin Lu

  2. Department of Electronics Engineering and Computer Science, Fukuoka University, Fukuoka, Japan

    Li Yujie

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Lu, Y. (2020). Non-uniformity Detection Method Based on Space-Time Autoregressive. In: Lu, H., Yujie, L. (eds) 2nd EAI International Conference on Robotic Sensor Networks. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-17763-8_15

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  • DOI: https://doi.org/10.1007/978-3-030-17763-8_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-17762-1

  • Online ISBN: 978-3-030-17763-8

  • eBook Packages: EngineeringEngineering (R0)

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