Advertisement

Radon-S transform for hypersonic maneuvering target detection

  • Jinping Sun
  • Xuwang Zhang
  • Peng LeiEmail author
  • Jun Wang
Article
  • 160 Downloads

Abstract

Hypersonic maneuvering target can cause complex range migration and Doppler frequency migration effects even in a very short time. This brings a big challenge to the common long-time coherent integration based target detection methods. To solve this problem, a novel hypersonic maneuvering target detection method called Radon-S transform is proposed in this paper on the basis of Radon transform and S-transform. It performs the coherent integration along the target track on the time–range plane, and then performs the non-coherent integration along the time–frequency curve of target echo on the time–frequency plane. By combining the two energy integration processes, the signal-to-noise/clutter ratio can be effectively improved. The definition of Radon-S transform, the concrete realization of detection process, and the setting of correlative parameters are introduced in detail. Then the performance of Radon-S transform is analyzed in theory. Finally, numerical experiment results show that the proposed method is superior to some common long-time coherent integration methods in the hypersonic maneuvering target detection.

Keywords

Radon-S transform Long-time coherent integration Time–frequency representation Hypersonic maneuvering target detection 

Notes

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (61471019).

References

  1. Barton, D. K. (2004). Radar system analysis and modeling. Beijing: Publishing House of Electronics Industry.Google Scholar
  2. Buzzi, S., Lops, M., & Venturino, L. (2005). Track-before-detect procedures for early detection of moving target from airborne radars. IEEE Transactions on Aerospace and Electronic Systems, 41(3), 937–954.CrossRefGoogle Scholar
  3. Chen, X. L., Guan, J., Liu, N. B., & He, Y. (2014). Maneuvering target detection via Radon-fractional Fourier transform-based long-time coherent integration. IEEE Transactions on Signal Processing, 62(4), 939–953.MathSciNetCrossRefzbMATHGoogle Scholar
  4. Dakovic, M., Thayaparan, T., & Stankovic, L. (2010). Time–frequency-based detection of fast manoeuvring targets. IET Signal Processing, 4(3), 287–297.CrossRefGoogle Scholar
  5. Daubechies, I., Lu, J. F., & Wu, H. T. (2011). Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis, 30(2), 243–261.MathSciNetCrossRefzbMATHGoogle Scholar
  6. Gelman, L., & Gould, J. (2008). A new time–frequency transform for non-stationary signals with any nonlinear instantaneous phase. Multidimensional Systems and Signal Processing, 19(2), 173–198.MathSciNetCrossRefzbMATHGoogle Scholar
  7. Grossi, E., Lops, M., & Venturino, L. (2013). A novel dynamic programming algorithm for track-before-detect in radar systems. IEEE Transactions on Signal Processing, 61(10), 2608–2619.MathSciNetCrossRefzbMATHGoogle Scholar
  8. Guan, J., Chen, X. L., Huang, Y., & He, Y. (2012). Adaptive fractional Fourier transform-based detection algorithm for moving target in heavy sea clutter. IET Radar, Sonar & Navigation, 6(5), 389–401.CrossRefGoogle Scholar
  9. Hu, J. F., Chen, H. W., Li, Y., Li, H. Y., & Xie, J. L. (2016). Moving target parameter estimation for MIMO radar based on the improved particle filter. Multidimensional Systems and Signal Processing.  https://doi.org/10.1007/s11045-016-0447-7.
  10. Huang, P. H., Liao, G. S., Yang, Z. W., Xia, X. G., Ma, J. T., & Ma, J. T. (2016a). Long-time coherent integration for weak maneuvering target detection and high-order motion parameter estimation based on keystone transform. IEEE Transactions on Signal Processing, 64(15), 4013–4026.CrossRefzbMATHGoogle Scholar
  11. Huang, Z. L., Zhang, J. Z., Zhao, T. H., & Sun, Y. B. (2016b). Synchrosqueezing S-transform and its application in seismic spectral decomposition. IEEE Transactions on Geoscience and Remote Sensing, 54(2), 817–825.CrossRefGoogle Scholar
  12. Li, G., Xia, X. G., & Peng, Y. N. (2008). Doppler keystone transform: An approach suitable for parallel implementation of SAR moving target imaging. IEEE Geoscience and Remote Sensing Letters, 5(4), 573–577.CrossRefGoogle Scholar
  13. Li, X. L., Cui, G. L., Yi, W., & Kong, L. J. (2016a). Manoeuvring target detection based on keystone transform and Lv’s distribution. IET Radar, Sonar & Navigation, 10(7), 1234–1242.CrossRefGoogle Scholar
  14. Li, X. L., Cui, G. L., Kong, L. J., & Yi, W. (2016b). Fast non-searching method for maneuvering target detection and motion parameters estimation. IEEE Transactions on Signal Processing, 64(9), 2232–2244.MathSciNetCrossRefzbMATHGoogle Scholar
  15. Millioz, F., & Davies, M. (2012). Sparse detection in the chirplet transform: Application to FMCW radar signals. IEEE Transactions on Signal Processing, 60(6), 2800–2813.MathSciNetCrossRefzbMATHGoogle Scholar
  16. Nelson, D. E., Starzyk, J. A., & David Ensley, D. (2003). Wavelet transformation and signal discrimination for HRR radar target recognition. Multidimensional Systems and Signal Processing, 14(1), 9–24.CrossRefzbMATHGoogle Scholar
  17. Rao, X., Tao, H. H., Xie, J., Su, J., & Li, W. P. (2015). Long-time coherent integration detection of weak manoeuvring target via integration algorithm, improved axis rotation discrete chirp-Fourier transform. IET Radar, Sonar & Navigation, 9(7), 917–926.CrossRefGoogle Scholar
  18. Skolnik, M., Linde, G., & Meads, K. (2001). Senrad: An advanced wideband air-surveillance radar. IEEE Transactions on Aerospace and Electronic Systems, 37(4), 1163–1175.CrossRefGoogle Scholar
  19. Skolnik, M. (2002). Introduction to radar system (3rd ed.). Columbus, OH: McGraw-Hill.Google Scholar
  20. Stankovic, L., Thayaparan, T., & Dakovic, M. (2006). Signal decomposition by using the S-method with application to the analysis of HF radar signals in sea-clutter. IEEE Transactions on Signal Processing, 54(11), 4332–4342.CrossRefzbMATHGoogle Scholar
  21. White, P. R., & Locke, J. (2012). Performance of methods based on the fractional Fourier transform for the detection of linear frequency modulated signals. IET Signal Processing, 6(5), 478–483.MathSciNetCrossRefGoogle Scholar
  22. Wu, L., Wei, X. Z., Yang, D. G., Wang, H. Q., & Li, X. (2012). ISAR imaging of targets with complex motion based on discrete chirp Fourier transform for cubic chirps. IEEE Transactions on Geoscience and Remote Sensing, 50(10), 4201–4212.CrossRefGoogle Scholar
  23. Xu, J., Yu, J., Peng, Y. N., & Xia, X. G. (2011a). Radon-Fourier transform for radar target detection, I: Generalized Doppler filter bank. IEEE Transaction on Aerospace and Electronic Systems, 47(2), 1186–1202.CrossRefGoogle Scholar
  24. Xu, J., Yu, J., Peng, Y. N., & Xia, X. G. (2011b). Radon-Fourier transform for radar target detection, II: Blind speed sidelobe suppression. IEEE Transactions on Aerospace Electronic Systems, 47(4), 2473–2489.CrossRefGoogle Scholar
  25. Zheng, J. B., Zhu, W. T., Su, T., & He, X. H. (2013). Novel algorithm for fast parametric detection of high-speed multi-target. Journal of Electronics & Information Technology (in Chinese), 35(2), 381–387.CrossRefGoogle Scholar
  26. Zheng, J. B., Su, T., Liu, H. W., Liao, G. S., Liu, Z., & Liu, Q. H. (2016). Radar high-speed target detection based on the frequency-domain deramp-keystone transform. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(1), 285–294.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.School of Electronic and Information EngineeringBeihang UniversityBeijingPeople’s Republic of China

Personalised recommendations