DOI QR코드

DOI QR Code

Joint FrFT-FFT basis compressed sensing and adaptive iterative optimization for countering suppressive jamming

  • Zhao, Yang (Department of Electronics and Optics, Army Engineering University) ;
  • Shang, Chaoxuan (Department of Electronics and Optics, Army Engineering University) ;
  • Han, Zhuangzhi (Department of Electronics and Optics, Army Engineering University) ;
  • Yin, Yuanwei (Department of Electronics and Optics, Army Engineering University) ;
  • Han, Ning (Department of Electronics and Optics, Army Engineering University) ;
  • Xie, Hui (Department of Electronics and Optics, Army Engineering University)
  • Received : 2018.07.06
  • Accepted : 2018.12.10
  • Published : 2019.06.03

Abstract

Accurate suppressive jamming is a prominent problem faced by radar equipment. It is difficult to solve signal detection problems for extremely low signal to noise ratios using traditional signal processing methods. In this study, a joint sensing dictionary based compressed sensing and adaptive iterative optimization algorithm is proposed to counter suppressive jamming in information domain. Prior information of the linear frequency modulation (LFM) and suppressive jamming signals are fully used by constructing a joint sensing dictionary. The jamming sensing dictionary is further adaptively optimized to perfectly match actual jamming signals. Finally, through the precise reconstruction of the jamming signal, high detection precision of the original LFM signal is realized. The construction of sensing dictionary adopts the Pei type fast fractional Fourier decomposition method, which serves as an efficient basis for the LFM signal. The proposed adaptive iterative optimization algorithm can solve grid mismatch problems brought on by undetermined signals and quickly achieve higher detection precision. The simulation results clearly show the effectiveness of the method.

References

  1. B. Wang, R. Gan, and J. Zhang, Overview of electronic countermeasure operational effectiveness evaluation, Elec. Inform. Warfare Tech. 32 (2017), no. 4, 54-60.
  2. G. Averbuch et al., Extracting low signal‐to‐noise ratio events with the Hough transform from sparse array data, Geophys. 83 (2018) no. 3, 43-51.
  3. R. Zhao and Y. Rui, Micro‐Doppler feature extraction method under low signal‐to‐noise ratio, Inform. Tech. 35 (2017), no. 6, 148-154.
  4. F. Ning et al., A highly efficient compressed sensing algorithm for acoustic imaging in low signal‐to‐noise ratio environments, Mechanical Syst. Signal Proc. 112 (2018), no. 6, 113-128. https://doi.org/10.1016/j.ymssp.2018.04.028
  5. D. Donoho, Compressed sensing, IEEE Trans. Information Theory 52 (2006), no. 4, 1289-1306. https://doi.org/10.1109/TIT.2006.871582
  6. K. Jin, D. Lee, and J. Ye, A general framework for compressed sensing and parallel MRI using annihilating filter based low‐rank Hankel matrix, IEEE Trans. Comp. Imaging 2 (2016), no. 4, 480-495. https://doi.org/10.1109/TCI.2016.2601296
  7. M. Nouri, M. Mivehchy, and S. Aghdam, Adaptive time-frequency kernel local fisher discriminant analysis to distinguish range deception jamming, Int. Conf. Comput. Commun. Netw. Tech., Denton, TX, USA, 2016, pp. 1-5.
  8. Y. Lu et al., Jointing time‐frequency distribution and compressed sensing for countering smeared spectrum jamming, J. Electronic Inform. Tech. 38 (2016), no. 12, 3275-3281.
  9. J. Huang, T. Zhang, and D. Metaxas, Learning with structured sparsity, 26th Annu. Int. Conf. Machine Learn., Montreal, Canada, 2011, pp. 417-424.
  10. Z. Zhang, B. Rao, Extension of SBL algorithms for the recovery of block sparse signals with intra‐block correlation, IEEE Trans. Sig. Process. 61 (2013), no. 8, 2009-2015. https://doi.org/10.1109/TSP.2013.2241055
  11. J. Starck and E. Candes, Very high quality image restoration by combining wavelets and curvelets, Proc. SPIE ‐ Int. Soc. Opt. Eng. 4478 (2001), 9-19.
  12. J. Starck, M. Elad, and D. Donoho, Redundant multiscale transforms and their application for morphological component separation, Adv Imag. Elec. Phys. 132 (2004), no. 4, 287-348.
  13. J. Bobin et al., Morphological component analysis: an adaptive thresholding strategy, IEEE Trans. Image Process. 16 (2007), no. 11, 2675-2681. https://doi.org/10.1109/TIP.2007.907073
  14. R. Fu, J. Li, and X. Gao, Static aurora images classification based on morphological component analysis, Acta Photonica Sinica 39 (2010), no. 6, 1034-1039. https://doi.org/10.3788/gzxb20103906.1034
  15. Y. Li, Y. Zhang, and X. Xu, Advances and perspective on morphological component analysis based on sparse representation, Acta Electron. Sinica 37 (2009), no. 1, 146-152. https://doi.org/10.3321/j.issn:0372-2112.2009.01.026
  16. R. Tao, B. Deng, and Y. Wang, Fractional Fourier Transform and Its Applications, Beijing, China, Tsinghua University Press, 2009, pp. 150-152.
  17. J. Starck, M. Elad, and D. Donoho, Image decomposition via the combination of sparse representations and a variational approach, IEEE Trans. Image Process. 14 (2005), no. 10, 1570-1582. https://doi.org/10.1109/TIP.2005.852206