高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于压缩感知理论的雷达成像技术与应用研究进展

李少东,  杨军,  陈文峰,  马晓岩

downloadPDF
文章导航 > 电子与信息学报  > 2016 >  38(2): 495-508
李少东, 杨军, 陈文峰, 马晓岩. 基于压缩感知理论的雷达成像技术与应用研究进展[J]. 电子与信息学报, 2016, 38(2): 495-508. doi: 10.11999/JEIT150874
引用本文: 李少东, 杨军, 陈文峰, 马晓岩. 基于压缩感知理论的雷达成像技术与应用研究进展[J]. 电子与信息学报, 2016, 38(2): 495-508. doi: 10.11999/JEIT150874
shu
LI Shaodong, YANG Jun, CHEN Wenfeng, MA Xiaoyan. Overview of Radar Imaging Technique and Application Based on Compressive Sensing Theory[J]. Journal of Electronics & Information Technology, 2016, 38(2): 495-508. doi: 10.11999/JEIT150874
Citation: LI Shaodong, YANG Jun, CHEN Wenfeng, MA Xiaoyan. Overview of Radar Imaging Technique and Application Based on Compressive Sensing Theory[J]. Journal of Electronics & Information Technology, 2016, 38(2): 495-508. doi: 10.11999/JEIT150874
shu

基于压缩感知理论的雷达成像技术与应用研究进展

doi: 10.11999/JEIT150874

Overview of Radar Imaging Technique and Application Based on Compressive Sensing Theory

    摘要
  • 摘要: 压缩感知理论基于信号稀疏性，将对信号采样转换为对信息自由度的采样，可大大降低采样率。而将压缩感知理论应用于雷达成像时有望在以下几个方面得到改善：增强成像性能，简化雷达硬件设计，缩短数据获取时间，减少数据量和传输量等。该文从压缩感知的稀疏性，压缩采样，无模糊重建3个关键步骤与成像雷达有机结合的角度，对近年来基于压缩感知理论的雷达成像技术研究现状进行系统综述，重点论述场景稀疏性与成像关系, 压缩采样方法(包括硬件)设计，场景图像快速高精度重建以及成像系统体制应用等方面，最后探讨了压缩感知理论应用尚需解决的问题和进一步发展方向。
    Abstract: Compressive Sensing (CS) theory, based on the sparsity of interested signal, samples degree-of-freedom of signal. CS is expected to improve the performance of imaging radar in the following aspects: improving the quality of imaging, simplifying the designing of radar hardware, shortening the imaging time and compressing data. This paper first combines the analysis of radar imaging with the three aspects of CS, namely the sparsity of interested signal, the compressive sampling and optimization method. Thereafter a particular and comprehensive review of CS theory in imaging radar is summarized, mainly including the relationship between sparsity of the scene and imaging, compressive sampling methods, fast and accurate reconstruction of the scene and the applications to different imaging radar systems. Finally, the unresolved problems in current research and further study directions are pointed out.
    • HTML全文
    • 参考文献(117)
  • ENDER J, AMIN M G, FORNARO G, et al. Recent advances in radar imagin [From the Guest Editors][J]. IEEE Signal Processing Magazine, 2014, 31(4), 15, 158.
    吴一戎, 洪 文, 张冰尘, 等. 稀疏微波成像研究进展(科普类) [J]. 雷达学报, 2014, 3(4): 383-395.
    WU Yirong, HONG Wen, ZHANG Bingchen, et al. Current developments of sparse microwave imaging[J]. Journal of Radars, 2014, 3(4): 383-395.
    杨俊刚. 利用稀疏信息的正则化雷达成像理论与方法研究 [D]. [博士论文], 国防科技大学, 2013.
    YANG Jungang. Research on sparsity-driven regularization radar imaging theory and method[D]. [Ph.D. dissertation], National University of Defense Technology, 2013.
    CANDES E. The restricted isometry property and its implication for compressed sensing[J]. Comptes Rendus Mathematique, 2008, 346(9/10): 589-592.
    BARANIUK R and STEEGHS P. Compressive radar imaging[C]. IEEE Radar Conference, Waltham, MA, 2007: 128-133.
    HERMAN M A and STROHMER T. High-resolution radar via compressed sensing[J]. IEEE Transactions on Signal Processing, 2009, 57(6): 2275-2284.
    刘记红, 徐少坤, 高勋章, 等. 压缩感知雷达成像技术综述 [J]. 信号处理, 2011, 27(2): 251-260.
    LIU Jihong, XU Shaokun, GAO Xunzhang, et al. A review of radar imaging technique based on compressed sensing[J] Journal of Signal Processing, 2011, 27(2): 251-260.
    ENDER J. On compressive sensing applied to radar[J]. Signal Processing, 2010, 90(5): 1402-1414.
    POTTER L C, ERTIN E, PARKER J T, et al. Sparsity and compressed sensing in radar imaging[J]. Proceedings of the IEEE, 2010, 98(6): 1006-1020.
    吴一戎. 稀疏微波成像的理论、体制和方法研究[R]. 中国科学院, 2010.
    ROSSI M, HAIMOVICH A M, and ELDAR Y C. Spatial compressive sensing for MIMO radar[J]. IEEE Transactions on Signal Processing, 2014, 62(2): 419-430.
    LIU Hongchao, JIU Bo, LIU Hongwei, et al. Super-resolution ISAR imaging based on sparse Bayesian learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(8): 5005-5013.
    贾琼琼, 吴仁彪. 一种波束域的高速空中动目标检测及参数估计方法[J]. 电子学报, 2014, 42(1): 14-19.
    JIA Qiongqiong and WU Renbiao. Detection and parameter estimation of fast moving air targets in beamspace[J]. Acta Electronica Sinica, 2014, 42(1): 14-19.
    WHITELONIS N and LING Hao. Radar signature analysis using a joint time-frequency distribution based on compressed sensing[J]. IEEE Transactions on Antennas and Propagation, 2014, 62(2): 755-763.
    WANG Yinghua, LIU Hongwei, and JIU Bo. PolSAR coherency matrix decomposition based on constrained sparse representation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(9): 5906-5922.
    吴敏, 邢孟道, 张磊. 基于压缩感知的二维联合超分辨 ISAR 成像算法[J]. 电子与信息学报, 2014, 36(1): 187-193. doi: 10.3724/SP.7.1146.2012.01597.
    WU Min, XING Mengdao, and ZHANG Lei. Two dimensional joint super-resolution ISAR imaging algorithm based on compressive sensing[J]. Journal of Electronics Infoumation Technology, 2014, 36(1): 187-193. doi: 10.3724/ SP. J.1146.2012.01597.
    KRICHENE H A, PEKALA M J, SHARP M D, et al. Compressive sensing and stretch processing[C]. IEEE Radar Conference, Georgia, USA, 2011: 362-367.
    ZHANG Xiaowei, LI Ming, ZUO Lei, et al. Compressed sensing detector for wideband radar using the dominant scatterer[J]. IEEE Signal Processing Letters, 2014, 21(10): 1275-1279.
    BAI Xueru, ZHOU Feng, BAO Zheng, et al. High-resolution radar imaging of space targets based on HRRP series[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(5): 2369-2381.
    ZHANG Lei, XING Mengdao, QIU Chengwei, et al. Resolution enhancement for inverse synthetic aperture radar imaging under low SNR via improved compressive sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(10): 3824-3838.
    RAO Wei, LI Gang, WANG Xiqin, et al. Comparison of parametric sparse recovery methods for ISAR image formation[J]. Scinece China Information Sciences, 2014, 57(12): 022315. doi: 10.1007/s11432-013-4859-9.
    李少东, 裴文炯, 杨 军, 等. 贝叶斯模型下的OMP重构算法及应用[J]. 系统工程与电子技术, 2015, 37(2): 246-251.
    LI Shaodong, PEI Wenjiong, YANG Jun, et al. OMP reconstructed algorithm via Bayesian model and its application[J]. Systems Engineering and Electronics, 2015, 37(2): 246-251.
    DU Xiaoyong, DUAN Chongwen, and HU Weidong. Sparse representation based auto-focusing technique for ISAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(3): 1826-1835.
    YANG Jungang, HUANG Xiaotao, and THOMPSON J. Compressed sensing radar imaging with compensation of observation position error[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(8): 4608-4620.
    XU Gang, XING Mengdao, and ZHANG Lei. Sparse apertures ISAR imaging and scaling for maneuvering targets [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(7): 2942-2956.
    XU Gang, XING Mengdao, and BAO Zheng. High-resolution inverse synthetic aperture radar imaging of maneuvering targets with sparse aperture[J]. Electronics Letters, 2015, 51(3): 287-289.
    GAO Xunzhang, LIU Zhen, CHEN Haowen, et al. Fourier-sparsity integrated method for complex target ISAR imagery[J]. Sensors, 2015, 15(2): 2723-2736. doi: 10.3390/ s150202723.
    WANG Lu, ZHAO Lifan, WAN Chunru, et al. Enhanced ISAR imaging by exploiting the continuity of the target scene [J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(9): 5736-5750.
    ZHANG Shunsheng, ZONG Zhulin, TIAN Zhong, et al. High-resolution bistatic ISAR imaging based on two-dimensional compressed sensing[J]. IEEE Transactions on Antennas and Propagation, 2015, 63(5): 2098-2111.
    刘振. 基于压缩感知的随机调制雷达信号处理方法与应用研究[D]. [博士论文], 国防科学技术大学, 2013.
    LIU Zhen. Methods and application of random modulated radar signal processing based on compressed sensing[D]. [Ph.D. dissertation], National University of Defense Technology, 2013.
    王法松, 张林让, 周宇. 压缩感知的多重测量向量模型与算法分析[J]. 信号处理, 2012, 28(6): 785-792.
    WANG Fasong, ZhANG Linrang, and ZHOU Yu. Multiple measurement vectors for compressed sensing: model and algorithms analysis[J]. Journal of Signal Processing, 2012, 28(6): 785-792.
    SCHMITT M and STILLA U. Compressive sensing based layover separation in airborne single-pass multi-baseline InSAR[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(2): 313-317.
    YU Tao, ZHANG Gong, and ZHANG Jin-dong. Guaranteed stability of sparse recovery in distributed compressive sensing MIMO radar[J] International Journal of Antennas and Propagation, 2015, Article ID 421740: 1-10.
    LIU H C, JIU B, LIU H W, et al. A novel ISAR imaging algorithm for micromotion targets based on multiple sparse Bayesian learning[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(10): 1772-1776.
    李少东, 陈文峰, 杨军, 等. 任意稀疏结构的多量测向量模型快速稀疏重构算法研究[J]. 电子学报, 2015, 43(4): 708-715.
    LI Shaodong, CHEN Wenfeng, YANG Jun, et al. Study on the fast sparse recovery algorithm via multiple measurement vectors of arbitrary sparse structure[J]. Acta Electronica Sinica, 2015, 43(4): 708-715.
    LIU Z, YOU P, WEI X Z, et al. Dynamic ISAR imaging of maneuvering targets based on sequential SL0[J] IEEE Geoscience and Remote Sensing Letters, 2013, 10(5): 1041-1045.
    李少东, 陈文峰, 杨军, 等. 一种快速复数线性Bregman迭代算法及其在ISAR成像中的应用[J]. 中国科学 信息科学, 2015, 45(9): 1179-1196. doi: 10.1360/N112014-00316.
    LI Shaodong, CHEN Wenfeng, YANG Jun, et al. A fast complex linearized Bregman iteration algorithm and its application in ISAR imaging[J] SCIENTIA SINICA Informationis, 2015, 45(9): 1179-1196. doi:10.1360/ N112014-00316.
    刘记红. 基于压缩感知的雷达成像技术研究 [D]. [博士论文], 国防科学技术大学, 2012.
    LIU Jihong. Inverse synthetic aperture radar imaging technique based on compressed sensing[D]. [Ph.D. dissertation], National University of Defense Technology, 2012.
    田文彪, 芮国胜, 张海波, 等. 一种面向二维观测模型的压缩感知重构算法[J]. 宇航学报, 2014, 35(9): 1072-1077.
    TIAN Wenbiao, RUI Guosheng, ZHANG Haibo, et al. A 2 dimensional measurement model-oriented compressed sensing reconstruction algorithm[J]. Journal of Astronautics, 2014, 35(9): 1072-1077.
    QIU Wei, ZHAO Hongzhong, ZHOU Jianxiong, et al. High-resolution fully polarimetric ISAR imaging based on compressive sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 52(10): 6119-6131.
    DONG Xiao and ZHANG Yunhua. A novel compressive sensing algorithm for SAR imaging[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(2): 708-720.
    YANG Jiefang and ZHANG Yunhua Novel compressive sensing-based dechirp-keystone algorithm for synthetic aperture radar imaging of moving target[J]. IET Radar, Sonar Navigation, 2015, 9(5): 509-518.
    BAE J H, KANG B S, KIM K T, et al. Performance of sparse recovery algorithms for the reconstruction of radar images from incomplete RCS data[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(4): 860-864.
    FANG Jian, XU Zongben, ZHANG Bingchen, et al. Fast compressed sensing SAR imaging based on approximated observation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(1): 352-363.
    LI Shiyong, ZHAO Guoqiang, LI Hou-min, et al. Near-field radar imaging via compressive sensing[J]. IEEE Transactions on Antennas and Propagation, 2015, 63(2): 828-833.
    SUN Shilong, ZHU Guofu, and JIN Tian. Novel methods to accelerate CS radar imaging by NUFFT[J] IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(1): 557-566.
    王强, 李佳, 沈毅. 压缩感知中确定性测量矩阵构造算法综述 [J]. 电子学报, 2013, 41(10): 2041-2050.
    WANG Qiang, LI Jia, and SHEN Yi . A survey on deterministic measurement matrix construction algorithms in compressive sensing[J]. Acta Electronica Sinica, 2013, 41(10): 2041-2050.
    张弓, 文方青, 陶宇, 等. 模拟信息转换器研究进展[J]. 系统工程与电子技术, 2015, 37(2): 229-238.
    ZHANG Gong, WEN Fangqing, TAO yu, et al. Advances in analog-to-information convertor[J]. Systems Engineering and Electronics, 2015, 37(2): 229-238.
    ILAN O B and ELDAR Y C. Sub-nyquist radar via Boppler focusing[J]. IEEE Transactions on Signal Processing, 2014, 62(7): 1796-1811..
    HOU Qingkai, LIU Yang, FAN Lijie, et al. Compressed sensing digital receiver and orthogonal reconstructing algorithm for wideband ISAR radar[J]. Science China Information Sciences, 2015, 58(2): 020302(10). doi: 10.1007/ s11432-014-5240-3.
    XI Feng, CHEN Shengyao, and LIU Zhong. Quadrature compressive sampling for radar signals[J]. IEEE Transactions on Signal Processing, 2014, 62(11): 2787-2802.
    ?LVAREZ Y, VAQUEIRO Y R, VALDES B G, et al. Phase error compensation in imaging systems using compressed sensing techniques[J]. IEEE Antennas and Wireless Propagation Letters, 2013, 12: 1574-1577.
    YIGIT E. Compressed sensing for millimeter-wave ground based SAR/ISAR imaging [J]. Jourual Infrared, Millimeter, and Terahertz Waves, 2014, 35(11): 932-948. doi: 10.1007/ s10762-014-0094-8.
    SERGE L S. Fast and robust compressive sensing method using mixed Hadamard sensing matrix[J]. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2012, 2(3): 353-361.
    ZHANG Jingxiong and YANG Ke. Informational analysis for compressive sampling in radar imaging[J]. Sensors, 2015, 15, 7136-7155. doi: 10.3390/s150407136.
    YAIR R and ADRIAN S. Compressed imaging with a separable sensing operator[J]. IEEE Signal Processing Letters, 2009, 16(6): 449-452.
    MARCO F D and RICHARD G B. Kronecker compressive sensing[J]. IEEE Transactions on Image Processing, 2012, 21(2): 494-504.
    KARABAYIRA O, YUCEDAGA O M, YUCEDAG S M, et al. Performance analysis of compressive ISAR imaging for complex targets[J]. Journal of Electromagnetic Waves and Applications, 2014, 28(10): 1236-1245.
    RAO Wei, LI Gang, WANG Xiqin, et al. Parametric sparse representation method for ISAR imaging of rotating targets [J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(2): 910-918.
    FENG Can, XIAO Liang, WEI Zhihui, et al. Compressive sensing Inverse Synthetic Aperture Radar imaging based on Gini index regularization[J]. International Journal of Automation and Computing, 2014, 11(4): 441-448.
    俞翔, 朱岱寅, 张劲东, 等. 基于设计结构化Gram矩阵的ISAR运动补偿方法[J]. 电子学报, 2014, 42(3): 452-461.
    YU Xiang, ZHU Daiyin, ZHANG Jindong, et al. A motion compensation algorithm based on the designing structured gram matrices[J]. Acta Electronica Sinica, 2014, 42(3): 452-461.
    KHWAJA A S and ZHANG X P. Compressed sensing ISAR reconstruction in the presence of rotational acceleration[J]. IEEE Transactions on Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(7): 2957-2970.
    黄大荣, 郭新荣, 张磊, 等. 稀疏孔径ISAR机动目标成像与相位补偿方法[J]. 航空学报, 2014, 35(7): 2019-2030.
    HUAN Darong, GUO Xinrong, ZHANG Lei, et al. ISAR phase compensation and imaging of maneuvering target with sparse apertures[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(7): 2019-2030.
    SUN Chao, WANG Baoping, FANG Yang, et al. High-resolution ISAR imaging of maneuvering targets based on sparse reconstruction[J]. Signal Processing, 2015, 108: 535-548.
    DU Xiaoyong, DUAN Chongwen, and HU Weidong. Sparse representation based autofocusing technique for ISAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(3): 1826-1835.
    TEKE O, GURBUZB A C, and ARIKAN O. A robust compressive sensing based technique for reconstruction of sparse radar scenes[J]. Digital Signal Processing, 2014, 27: 23-32.
    WANG Tianyun, LU Xinfei, YU Xiaofei, et al. A fast and accurate sparse continuous signal reconstruction by homotopy DCD with non-convex regularization[J]. Sensors. 2014, 14(4): 5929-5951. doi: 10.3390/s140405929.
    ZHAO Li-fan, WANG Lu, and BI Guoan. An. autofocus technique for high-resolution inverse synthetic aperture radar imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(10): 6392-6403.
    YANG Jungang, THOMPSON J, HUANG Xiaotao, et al. Random-frequency SAR imaging based on compressed sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(2): 983-994.
    HUANG Tianyao, LIU Yimin, MENG Huadong, et al. Cognitive random stepped frequency radar with sparse recovery[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(2): 858-870.
    WANG Xiao, XU Feng, and JIN Yaqiu. Numerical simulation of tomographic-SAR imaging and object reconstruction using compressive sensing with L1/2-norm regularization[J]. Chinese Science Bulletin, 2014, 59(33): 4600-4607.
    MROZACK A, HEIMBECK M, MARKS D L, et al. Compressive and adaptive millimeter-wave SAR[OL]. arXiv preprint arXiv:1402.1466, 2014.
    ?ETIN M, STOJANOVIC I, ?NHON N O, et al. Sparsity-driven synthetic aperture radar imaging: reconstruction, autofocusing, moving targets, and compressed sensing[J]. IEEE Signal Processing Magazine, 2014, 31(4): 27-40.
    AGUILERA E, NANNINI M, and REIGBER A. A data-adaptive compressed sensing approach to polarimetric SAR tomography of forested areas[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(3): 543-547.
    ZHU Xiaoxiang and BAMLER R. Super resolving SAR tomography for multidimensional imaging of urban areas: compressive sensing-based tomoSAR inversion[J]. IEEE Signal Processing Magzine, 2014, 31(4): 51-58.
    SCHMITTM and STILLA U. Compressive sensing based layover separation in airborne single-pass multi-baseline InSAR data[J]. IEEE Transactions on Geoscience and Remote Sensing Letters, 2013, 10(2): 313-317.
    HOU Xingsong, ZHANG Lan, GONG Chen, et al. SAR image Bayesian compressive sensing exploiting the interscale and intrascale dependencies in directional lifting wavelet transform domain[J]. Neurocomputing, 2014, 133: 358-368.
    SHEN Fangfang, ZHAO Guanghui, SHI Guangming, et al. Compressive SAR imaging with joint sparsity and local: similarity exploitation[J]. Sensors, 2015, 15(2), 4176-4192. doi: 10.3390/s150204176.
    VAQUEIRO Y R, LOPEZ Y A, VALDES B G, et al. On the use of compressed sensing techniques for improving multistatic millimeter-wave portal-based personnel screenings[J]. IEEE Transactions on Antennas and Propagation, 2014, 62(1): 494-499.
    ZHANG Shunsheng, ZHANG Wei, ZONG Zhulin, et al. High-resolution bistatic ISAR imaging based on two-dimensional compressed sensing[J]. IEEE Transactions on Antennas and Propagation, 2015, 63(5): 2098-2111.
    QIU Wei, ZHAO Hongzhong, ZHOU Jianxiong, et al. High-resolution fully polarimetric ISAR imaging based on compressive sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(10): 6119-6131.
    LI Liechen, LI Daojing, LIU Bo, et al. Complex-valued interferometric inverse synthetic aperture radar image compression base on compressed sensing[J]. The Journal of Engineering, 2014. doi: 10.1049/joe.2014.0033.
    LIU Yabo, LI Ning, WANG R, et al. Achieving High-Quality Three-dimensional InISAR imageries of maneuvering target via super-resolution ISAR Imaging by exploiting sparseness [J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(4): 828-832.
    ZHANG Xiaohua, BAI Ting, MENG Hongyun, et al. Compressive sensing-based ISAR imaging via the combination of the sparsity and nonlocal total variation[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(5): 990-994.
    CHAI Shougang, CHEN Weidong, CHEN Chang, et al. Sparse fusion imaging for a moving target in T/R-R configuration[J]. Sensors, 2014, 14(6), 10664-10679. doi: 10. 3390/s140610664.
    朱江, 廖桂生, 朱圣棋. 基于块稀疏的空间碎片群目标成像方法[J]. 电子与信息学报, 2015, 37(3): 587-593. doi: 10.11999/ JEIT140509.
    ZHU Jiang, LIAO Guisheng, and ZHU Shengqi. Space group debris imaging based on block-sparse method[J]. Journal of Electronics Information Technology, 2015, 37(3): 587-593. doi: 10.11999/JEIT140509.
    顾福飞, 池龙, 张群, 等. 基于压缩感知的稀疏阵列MIMO雷达成像方法[J]. 电子与信息学报, 2011, 33(10): 2452-2456. doi: 10.11999/JEIT140509. doi: 10.3724/SP.J.1146.2011.00287.
    GU Fufei, CHI Long, ZHANG Qun, et al. An imaging method for MIMO radar with sparse array based on compressed sensing[J]. Journal of Electronics Information Technology, 2011, 33(10): 2452-2456. doi: 10.3724/SP.J.1146.2011.00287.
    ZHANG Baoju and WANG Wei. Through-wall detection of human being with compressed UWB radar data[J]. EURASIP Journal on Wireless Communications and Networking, 2013, 2013: 162. doi: 10.1186/1687-1499- 2013-162.
    YANG Jungang, JIN Tian, HUANG Xiaotao, et al. Sparse MIMO array forward-looking GPR imaging based on compressed sensing in clutter environment[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(7): 4480-4494.
    AMIN M G. Compressive Sensing for Urban Radar[M]. Boca Raton, London, New York. CRC Press, 2015.
    LI Kezhi, GAN Lu, and LING Cong. Convolutional compressed sensing using deterministic sequences[J]. IEEE Transactions on Signal Processing, 2014, 61(3): 740-752.
    ZHANG Xiaowei, LI Ming, ZUO Lei, et al. Adaptive subspace detection for wideband radar using sparsity in Sinc basis[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(11): 1916-1920..
    DAVENPORT M A, BOUFOUNOS P T, WAKIN M B, et al. Signal processing with compressive measurements[J]. IEEE Journal of Selected Topics in Signal Processing, 2010, 4(2): 445-460.
    荆楠, 毕卫红, 胡正平, 等. 动态压缩感知综述[J]. 自动化学报, 2015, 41(1): 22-36.
    JING Nan, BI Weihong, HU Zhengping, et al. A survey on dynamic compressed sensing[J]. Acta Automatica Sinica. 2015, 41(1): 22-36.
    刘芳, 武娇, 杨淑媛, 等. 结构化压缩感知研究进展[J]. 自动化学报, 2013, 39(12): 1980-1995.
    LIU Fang, WU Jiao, YANG Shuyuan, et al. Research advances on structured compressive sensing[J]. Acta Automatica Sinica, 2013, 39(12): 1980-1995.
    刘建伟, 崔立鹏, 罗雄麟. 组稀疏模型及其算法综述[J]. 电子学报, 2015, 43(4): 776-782.
    LIU Jianwei, CUI Lipeng, and LUO Xionglin. Survey on group sparse models and algorithms[J]. Acta Electronica Sinica, 2015, 43(4): 776-782.
    • 相关文章
    • 施引文献
  • 资源附件(0)
  • 加载中
计量
  • 文章访问数:  2427
  • HTML全文浏览量:  223
  • PDF下载量:  1262
  • 被引次数: 0
出版历程
  • 收稿日期:  2015-07-21
  • 修回日期:  2015-12-08
  • 刊出日期:  2016-02-19

目录

    /

    返回文章
    返回

    Copyright © 2018《电子与信息学报》编辑部 京ICP备20021838号-8

    中国科学院电子学研究所,北京市2702信箱,邮编：100190

    电话：010-58887066传真：021-64253812Email：jeit@mail.ie.ac.cn

    本系统由 北京仁和汇智信息技术有限公司 开发    百度统计