Full Text:   <3718>

CLC number: TN4

On-line Access: 2018-02-06

Received: 2016-06-06

Revision Accepted: 2016-11-28

Crosschecked: 2017-12-20

Cited: 0

Clicked: 6729

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.12 P.2058-2069


Ultra-wideband FMCW ISAR imaging with a large rotation angle based on block-sparse recovery

Author(s):  Ke Jin, Tao Lai, Gong-quan Li, Ting Wang, Yong-jun Zhao

Affiliation(s):  Zhengzhou Institute of Information Science and Technology, Zhengzhou 450000, China

Corresponding email(s):   jk83302536@126.com

Key Words:  Frequency modulated continuous wave (FMCW), Inverse synthetic aperture radar (ISAR), Match-filter-based algorithm, Compressed sensing, Block sparsity

Ke Jin, Tao Lai, Gong-quan Li, Ting Wang, Yong-jun Zhao. Ultra-wideband FMCW ISAR imaging with a large rotation angle based on block-sparse recovery[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(12): 2058-2069.

@article{title="Ultra-wideband FMCW ISAR imaging with a large rotation angle based on block-sparse recovery",
author="Ke Jin, Tao Lai, Gong-quan Li, Ting Wang, Yong-jun Zhao",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Ultra-wideband FMCW ISAR imaging with a large rotation angle based on block-sparse recovery
%A Ke Jin
%A Tao Lai
%A Gong-quan Li
%A Ting Wang
%A Yong-jun Zhao
%J Frontiers of Information Technology & Electronic Engineering
%V 18
%N 12
%P 2058-2069
%@ 2095-9184
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601310

T1 - Ultra-wideband FMCW ISAR imaging with a large rotation angle based on block-sparse recovery
A1 - Ke Jin
A1 - Tao Lai
A1 - Gong-quan Li
A1 - Ting Wang
A1 - Yong-jun Zhao
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 12
SP - 2058
EP - 2069
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1601310

Ultra-wideband frequency modulated continuous wave (FMCW) radar has the ability to achieve high-range resolution. Combined with the inverse synthetic aperture technique, high azimuth resolution can be realized under a large rotation angle. However, the range-azimuth coupling problem seriously restricts the inverse synthetic aperture radar (ISAR) imaging performance. Based on the turntable model, traditional match-filter-based, range Doppler algorithms (RDAs) and the back projection algorithm (BPA) are investigated. To eliminate the sidelobe effects of traditional algorithms, compressed sensing (CS) is preferred. Considering the block structure of a signal at high resolution, a block-sparsity adaptive matching pursuit algorithm (BSAMP) is proposed. By matching pursuit and backtracking, a signal with unknown sparsity can be recovered accurately by updating the support set iteratively. Finally, several experiments are conducted. In comparison with other algorithms, the results from processing the simulation data, some simple targets, and a complex target indicate the effectiveness and superiority of the proposed algorithm.


概要:超宽带调频连续波雷达可凭借大带宽获得极高的距离分辨率。同时,结合逆合成孔径技术,在大转角条件下进一步得到二维高分辨目标图像。然而,在逆合成孔径雷达中的距离-方位耦合问题严重制约了成像性能。本文基于转台模型的假设,研究传统的匹配滤波类算法,如距离多普勒算法和后向投影算法。为消除传统算法中的高旁瓣效应,进一步探索压缩感知类算法。考虑到高分辨条件下信号的块稀疏特性,本文重点研究了基于块稀疏的自适应匹配追踪大转角ISAR(inverse synthetic aperture radar)成像算法。通过信号对字典的匹配追踪与回溯更新,将雷达回波在未知稀疏度下精确重构,最终得到目标ISAR图像。计算机仿真、简单目标和复杂目标实验结果证明了该算法相比其他算法的有效性和优越性。


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Anghel, A., Vasile, G., Cacoveanu, R., et al., 2014. Short-range wideband FMCW radar for millimetric displacement measurements. IEEE Trans. Geosci. Remote Sens., 52(9):5633-5642.

[2]Anghel, A., Vasile, G., Cacoveanu, R., et al., 2015. Range autofocusing for FMCW radars using time warping and a spectral concentration measure. IEEE Radar Conf., p.581-586.

[3]Do, T.T., Gan, L., Nguyen, N., et al., 2008. Sparsity adaptive matching pursuit algorithm for practical compressed sensing. 42nd Asilomar Conf. on Signals, p.581-587.

[4]Eldar, Y.C., Kuppinger, P., Bolcskei, H., 2010. Block-sparse signals: uncertainty relations and efficient recovery. IEEE Trans. Signal Process., 58(6):3042-3054.

[5]Elhamifar, E., Vidal, R., 2012. Block-sparse recovery via convex optimization. IEEE Trans. Signal Process., 60(8): 4094-4107.

[6]Fu, Y.W., Li, Y.N., Li, X., 2012. A 3D InISAR imaging method for non-uniformly rotating target based on match. Four. Transf. J. Astron., 33(6):769-775 (in Chinese).

[7]Herman, M.A., Strohmer, T., 2009. High-resolution radar via compressed sensing. IEEE Trans. Signal Process., 57(6): 2275-2284.

[8]Hu, J.M., Jiang, W.D., Fu, Y.W., et al., 2010. A novel range alignment algorithm for ISAR. IEEE 2nd Int. Conf. on Computer Engineering and Technology, p.358-362.

[9]Kim, K.T., Seo, D.K., Kim, H.T., 2005. Efficient classification of ISAR images. IEEE Trans. Antennas Propag., 53(5): 1611-1621.

[10]Li, G., Zhang, H., Wang, X.Q., et al., 2012. ISAR 2D imaging of uniformly rotating targets via matching pursuit. IEEE Trans. Aerosp. Electron. Syst., 48(2):1838-1846.

[11]Li, S.J., Qi, H.R., 2014a. Compressed dictionary learning for detecting activations in fMRI using double sparsity. IEEE Global Conf. on Signal and Information Processing, p.434-437.

[12]Li, S.J., Qi, H.R., 2014b. Recursive low-rank and sparse recovery of surveillance video using compressed sensing. Proc Int. Conf. on Distributed Smart Cameras, Article 1.

[13]Li, S.J., Qi, H.R., 2015. A Douglas-Rachford splitting approach to compressed sensing image recovery using low-rank regularization. IEEE Trans. Image Process., 24(11): 4240-4249.

[14]Lu, G.Y., Bao, Z., 1999. Analysis of MTRC compensation algorithm in ISAR. IEEE Radar Conf., p.242-245.

[15]Martorella, M., Berizzi, F., Haywood, B., 2005. Contrast maximisation based technique for 2D ISAR autofocusing. IEE Proc. Radar Son. Navig., 152(4):253-262.

[16]Meta, A., Hoogeboom, P., Ligthart, L.P., 2007. Signal processing for FMCW SAR. IEEE Trans. Geosci. Remote Sens., 45(11):3519-3532.

[17]Middleton, R.J.C., Macfarlane, D.G., Robertson, D.A., 2011. Range autofocus for linearly frequency-modulated continuous wave radar. IET Radar Sonar Navig., 5(3):288-295.

[18]Özdemir, C., 2012. Inverse synthetic aperture radar imaging with MATLAB algorithms. John Wiley & Sons, Hoboken, New Jersey, America.

[19]Qiu, X.H., Zhao, Y., 2006. A non-parametric rotating angle acquisition method for optimal ISAR imaging. IEEE Antennas and Propagation Society Int. Symp., p.2697-2700.

[20]Ribalta, A., 2011. Time-domain reconstruction algorithms for FMCW-SAR. IEEE Geosci. Remote Sens. Lett., 8(3):396-400.

[21]Wang, H.X., Liang, Y., Xing, M.D., et al., 2011. ISAR imaging via sparse frequency-stepped chirp signal. Sci. China Inform., 55(4):877-888.

[22]Wang, R., Loffeld, O., Nies, H., et al., 2010. Focus FMCW SAR data using the wavenumber domain algorithm. IEEE Trans. Geosci. Remote Sens., 48(4):2109-2118.

[23]Wang, S.L., Li, S.G., Ni, J.L., et al., 2001. A new transform-match Fourier transform. Acta Electron. Sin., 29(3):403-405 (in Chinese).

[24]Wang, W.W., Liao, G.S., Zhang, L., et al., 2012. An imaging method based on compressive sensing for sparse aperture of SAR. Acta Electron. Sin., 40(12):2487-2494 (in Chinese).

[25]Xing, M.D., Wu, R.B., Lan, J.Q., et al., 2004. Migration through resolution cell compensation in ISAR imaging. IEEE Geosci. Remote Sens. Lett., 1(2):141-144.

[26]Zhang, S.S., Xiao, B., Zong, Z.L., 2014. Improved compressed sensing for high-resolution ISAR image reconstruction. Chin. Sci. Bull., 59(23):2918-2926.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE