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2017-07-10
3-d Imaging of High-Speed Moving Space Target via Joint Parametric Sparse Representation
By
Progress In Electromagnetics Research M, Vol. 58, 125-134, 2017
Abstract
The high-speed moving of space targets introduces distortion and migration to range profile, which will have a negative effect on three-dimensional (3-D) imaging of targets. In this paper, based on joint parametric sparse representation, a 3-D imaging method for high-speed moving space target is proposed. First, the impact of high speed on range profile of target is analyzed. Then, based on an L-shaped three-antenna interferometric system, a dynamic joint parametric sparse representation model of echoes from three antennas is established. The dictionary matrix is refined by iterative estimation of velocity. Moreover, an improved orthogonal matching pursuit (OMP) algorithm is proposed to recover interferometric phase information. Finally, with the phase information, interferometric processing is conducted to obtain the 3-D image of target scatterers. The simulation results verify the effectiveness of the proposed method.
Citation
Yuxue Sun, Meng Jiang, Ying Luo, Qun Zhang, and Chunhui Chen, "3-d Imaging of High-Speed Moving Space Target via Joint Parametric Sparse Representation," Progress In Electromagnetics Research M, Vol. 58, 125-134, 2017.
doi:10.2528/PIERM17041401
References

1. Zhang, L., M. D. Xing, C. W. Qiu, and Z. Bao, "Two-dimensional spectrum matched filter banks for high-speed spinning-target three-dimensional IASR imaging," IEEE Geosci. Remote Sens. Lett., Vol. 6, No. 3, 368-372, 2009.
doi:10.1109/LGRS.2009.2013487

2. Wang, Q., M. D. Xing, L. G. Yue, and Z. Bao, "High-resolution three-dimensional radar imaging for rapidly spinning targets," IEEE Trans. Geosci. Remote Sens., Vol. 46, No. 1, 22-30, 2008.
doi:10.1109/TGRS.2007.909086

3. Bai, X. R., M. D. Xing, F. Zhou, and Z. Bao, "High-resolution three-dimensional imaging of spinning space debris," IEEE Trans. on Geosci. Remote Sens., Vol. 47, No. 4, 2352-2362, 2009.

4. Ai, X. F., Y. Huang, F. Zhao, J. H. Yang, Y. Z. Li, and S. P. Xiao, "Imaging of spinning targets via narrow-band T/R-R bistatic radars," IEEE Geosci. Remote Sens. Lett., Vol. 10, No. 2, 362-366, 2013.
doi:10.1109/LGRS.2012.2205893

5. Luo, Y., Q. Zhang, N. Yuan, F. Zhu, and F. F. Gu, "Three-dimensional precession feature extraction of space targets," IEEE Trans. Aerosp. Electron. Syst., Vol. 50, No. 2, 1313-1329, 2014.
doi:10.1109/TAES.2014.110545

6. Sun, Y. X., C. Z. Ma, Y. Luo, Y. A. Chen, and Q. Zhang, "An interferometric-processing based three-dimensional imaging method for space rotating targets," IET 2016 International Radar Conference, 398-402, Guangzhou, China, October 2016.

7. Xing, M. D., Q. Wang, G. Y. Wang, and Z. Bao, "A matched-filter-bank-based 3-D imaging algorithm for rapidly spinning targets," IEEE Trans. Geosci. Remote Sens., Vol. 47, No. 7, 2106-2113, 2009.
doi:10.1109/TGRS.2008.2010499

8. Pang, C. S., T. Shan, T. Ran, and N. Zhang, "Detection of high-speed and accelerated target based on the linear frequency modulation radar," IET Radar Sonar Navig., Vol. 8, No. 1, 37-47, 2014.
doi:10.1049/iet-rsn.2013.0001

9. Chen, Q. Q., G. Xu, L. Zhang, M. D. Xing, and Z. Bao, "Three-dimensional interferometric inverse synthetic aperture radar imaging with limited pulses by exploiting joint sparsity," IET Radar Sonar Navig., Vol. 9, No. 6, 692-701, 2015.
doi:10.1049/iet-rsn.2014.0275

10. Liu, Y. B., N. Li, R. Wang, and Y. K. Deng, "Achieving high-quality three-dimensional InISAR imageries of maneuvering target via super-resolution ISAR imaging by exploiting sparseness," IEEE Geosci. Remote Sens. Lett., Vol. 11, No. 4, 828-832, 2014.
doi:10.1109/LGRS.2013.2279402

11. Liao, K., H. Chen, and D. Y. Zhou, "High speed motion compensation based on the Fractional Fourier transform," 2013 IEEE 4th International Conference on Electronics Information and Emergency Communication, 169-172, Beijing, China, November 2013.