High-speed Target ISAR Imaging via Compressed Sensing Based on Sparsity in Fractional Fourier Domain

LIU Jihong; LI Xiang; GAO Xunzhang; ZHUANG Zhaowen

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Article Navigation > Chinese Journal of Electronics > 2013 > 22(3): 648-654

LIU Jihong, LI Xiang, GAO Xunzhang, et al., “High-speed Target ISAR Imaging via Compressed Sensing Based on Sparsity in Fractional Fourier Domain,” Chinese Journal of Electronics, vol. 22, no. 3, pp. 648-654, 2013,

Citation:

LIU Jihong, LI Xiang, GAO Xunzhang, et al., “High-speed Target ISAR Imaging via Compressed Sensing Based on Sparsity in Fractional Fourier Domain,” Chinese Journal of Electronics, vol. 22, no. 3, pp. 648-654, 2013,

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High-speed Target ISAR Imaging via Compressed Sensing Based on Sparsity in Fractional Fourier Domain

School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China

Funds: This work is supported by the National Science Fund for Distinguished Young Scholars of China (No.61025006) and National Natural Science Foundation of China (No.61171133).

Received Date: 2011-03-01
Rev Recd Date: 2012-10-01
Publish Date: 2013-06-15

Abstract

Abstract

Compressed sensing (CS) provides great potential to reduce radar sampling rate while improve the imaging performance. In this paper, the application of CS to ISAR imaging of high-speed space targets is introduced. Firstly, based on the analysis of the echo model of highspeed targets, we elucidate that the dechirped high-speed target echo is of sparsity in fractional Fourier domain. Then the Analog-to-information conversion (AIC) is used to take compressive measurements, following which the radar image can be recovered via nonlinear optimization. In particular, considering the non-cooperative characteristic of targets, an optimization search algorithm based on sparsity of the reconstructed range profiles is presented, so as to find the optimal transform order of fractional Fourier transform. Experiment results from both simulated data and measured data show the validity and superiority of the proposed imaging method.
- ISAR imaging,
- High-speed target,
- Compressed sensing,
- Fractional fourier transform,
- Sparse dictionary

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References(18)

References

S.L. Wen, Q. Yuan, Z.Y. Qin, “Error acquisition and compensation for wideband linear frequency modulated signal Stretch processing”, Systems Engineering and Electronics, Vol.27, No.1, pp.36-39, 2005. (in Chinese)

E.J. Cand`es, M.B. Wakin, “An introduction to compressive sampling”, IEEE Signal Process. Mag., Vol.25, No.2, pp.2130, 2008.

L.C. Jiao, S.Y. Yang, F. Liu et al., “Development and prospect of compressive sensing”, Acta Electronica Sinica, Vol.39, No.7, pp.1651-1662, 2011. (in Chinese)

G.H. Jin, X.Z. Gao, X. Li et al., “A high velocity motion compensation algorithm of ISAR echo based on chirplet transform”, Journal of Astronautics, Vol.31, No.7, pp.1844-1849, 2010. (in Chinese)

L. Wu, X.Z. Wei, D.G. Yang et al., “Accurate motion estimation of high speed moving target about stepped-frequency chirp signal”, Acta Electronica Sinica, Vol.38, No.12, pp.2832-2838, 2010. (in Chinese)

M. Cao, “Research on high resolution radar imaging technology for space targets”, Ph.D. Thesis, National University of Defense Technology, 2009.

J.H.G. Ender, “On compressive sensing applied to radar”, Signal Process., Vol.90, No.5, pp.1402-1414, 2010.

L.C. Potter, E. Ertin, J.T. Parker, M. Cetin, “Sparsity and compressed sensing in radar imaging”, Proceedings of the IEEE, Vol.98, No.6, pp.1006-1020, 2010.

H.X. Wang, Y.H. Quan, M.D. Xing et al., “ISAR imaging via sparse probing frequencies”, IEEE Geosci. Remote Sens. Lett., Vol.8, No.3, pp.451-455, 2011.

Z. Bao, M.D. Xing, T. Wang, Radar Imaging Technique, Beijing: Publishing House of Electronics Industry, 2006.

H.M. Ozaktas, M.A. Kutay, “Digital computation of the fractional Fourier transform”, IEEE Trans. Signal Process., Vol.44, No.9, pp.2141-2150, 1996.

R. Tao, F. Zhang, Y. Wang, “Progress in the discretization of fractional Fourier transform”, Sci. China Inf. Sci., Vol.38, No.4, pp.481-503, 2008. (in Chinese)

J.N. Laska, S. Kirolos, M.F. Duarte et al., “Theory and implementation of an analog-to-information converter using random demodulation”, Proc. IEEE Int. Sym. Circuits and System, New Orleans, LA, pp.1959-1962, 2007.

L. Zhang, M.D. Xing, C.W. Qiu et al., “Resolution enhancement for inversed synthetic aperture radar imaging under Low SNR via improved compressive sensing”, IEEE Trans. Geosci. Remote Sens., Vol.48, No.10, pp.3824-3838, 2010.

L. Applebaum, S. Howard, S. Searle et al., “Chirp sensing codes: Deterministic compressed sensing measurements for fast recovery”, Appl. Comput. Harmon. Anal., Vol.26, No.2, pp.283290, 2009.

H. Mohimani, M. Babaie-Zadeh, C. Jutten, “A fast approach for overcomplete sparse decomposition based on smoothed l0 norm”, IEEE Trans. Signal Process., Vol.57, No.1, pp.289-301, 2009.

C.F. Gerald, P.O. Wheatley, Applied Numerical Analysis, Seventh Edition, Addison-Wesley, 2004.

Y.H. Zhang, Y.P. Zhu, P. You et al., “Wideband echo simulation for high-speed space target”, Journal of System Simulation, Vol.21, No.9, pp.2721-2724, 2729, 2009. (in Chinese)

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