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

采用复合三角函数实现MIMO雷达单快拍成像的平滑l0范数改进算法

童宁宁 赵小茹 丁姗姗 何兴宇

童宁宁, 赵小茹, 丁姗姗, 何兴宇. 采用复合三角函数实现MIMO雷达单快拍成像的平滑l0范数改进算法[J]. 电子与信息学报, 2017, 39(12): 2803-2810. doi: 10.11999/JEIT170294
引用本文: 童宁宁, 赵小茹, 丁姗姗, 何兴宇. 采用复合三角函数实现MIMO雷达单快拍成像的平滑l0范数改进算法[J]. 电子与信息学报, 2017, 39(12): 2803-2810. doi: 10.11999/JEIT170294
TONG Ningning, ZHAO Xiaoru, DING Shanshan, HE Xingyu. Improved Smoothed l0 Norm Algorithm for MIMO Radar Signal Snapshot Imaging via Composite Trigonometric Function[J]. Journal of Electronics & Information Technology, 2017, 39(12): 2803-2810. doi: 10.11999/JEIT170294
Citation: TONG Ningning, ZHAO Xiaoru, DING Shanshan, HE Xingyu. Improved Smoothed l0 Norm Algorithm for MIMO Radar Signal Snapshot Imaging via Composite Trigonometric Function[J]. Journal of Electronics & Information Technology, 2017, 39(12): 2803-2810. doi: 10.11999/JEIT170294

采用复合三角函数实现MIMO雷达单快拍成像的平滑l0范数改进算法

doi: 10.11999/JEIT170294
基金项目: 

国家自然科学基金(6157010318)

Improved Smoothed l0 Norm Algorithm for MIMO Radar Signal Snapshot Imaging via Composite Trigonometric Function

Funds: 

The National Natural Science Foundation of China (6157010318)

  • 摘要: SL0算法采用最速下降法和梯度投影原理,将选取的平滑函数逼近l0范数,以求解最优化问题实现信号重建。针对平滑函数逼近性能、算法精确度和算法运算量3个方面进行研究,该文提出一种高效地实现信号重构的算法 ICTF-SL0算法。首先,选取复合三角函数作为平滑函数,同时以加权的方式引入全变差(Total Variation, TV)设定约束条件;其次,采用Chaotic迭代代替矩阵分解实现梯度投影。仿真结果证明,相比SL0及其他改进算法,ICTF-SL0能够提高成像精度,降低运算负担,实现稀疏阵列MIMO雷达单快拍成像。
  • HU Xiaowei, TONG Ningning, ZhANG Yongshun, et al. Multiple-Input-Multiple-Output (MIMO) radar super- resolution three-dimensional imaging based on a dimension- reduction compressive sensing[J]. IET Radar, Sonar Navigation, 2016, 10(4): 757-764. doi: 10.1049/iet-rsn. 2015.0345.
    杨建超, 苏卫民, 顾红. 基于二维频率估计的MIMO-ISAR空时二维回波重排方法[J]. 电子与信息学报, 2014, 36(9): 2180-2186. doi: 10.3724/SP.J.1146.2013.01558.
    YANG Jian-chao, SU Wei-min, and GU Hong. A method for rearrangement of 2D MIMO-ISAR space-time echo based on 2D frequency estimation[J]. Journal of Electronics Information Technology, 2014, 36(9): 2180-2186. doi: 10.3724 /SP.J.1146.2013.01558.
    陈刚. 稀布阵列MIMO雷达成像技术研究[D]. [博士论文], 南京理工大学, 2014.
    CHEN Gang. Research on techniques for sparse array MIMO radar imaging[D]. [Ph.D. dissertation], Nanjing University of Science Technology, 2014.
    GU Fufei, CHI Long, ZHANG Qun, et al. Single snapshot imaging method in Multiple-Input Multiple-Output radar with sparse antenna array[J]. IET Radar, Sonar Navigation, 2013, 7(5): 535-543. doi: 10.1049/iet-rsn.2011.0363.
    DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306. doi: 10.1109/ TIT.2006.871582.
    文方青, 张弓, 陶宇, 等. 面向低信噪比的自适应压缩感知方法[J]. 物理学报, 2015, 64(8): 084301-1-084301-8. doi: 10.7498 /aps.64.084301.
    WEN Fangqing, ZHANG Gong, TAO Yu, et al. Adaptive compressive sensing toward low signal-to-noise ratio scene[J]. Acta Physics Sinica, 2015, 64(8): 084301-1-084301-8. doi: 10.7498/aps.64.084301.
    徐浩, 朱宇, 刘杰, 等. 集群微波遥感卫星稀疏随机分布构型成像[J]. 测绘通报, 2014(S1): 32-35. doi: 10.13474/j.cnki. 11-2246.2014.0608.
    XU Hao, ZHU Yu, LIU Jie, et al. The imaging of cluster microwave remote sensing satellites based on the sparse random distribution[J]. Bulletin of Surveying and Mapping, 2014(S1): 32-35. doi: 10.13474/j.cnki.11-2246.2014.0608.
    胡晓伟, 童宁宁, 何兴宇, 等. 基于Kronecker压缩感知的宽带MIMO雷达高分辨三维成像[J]. 电子与信息学报, 2016, 38(6): 1475-1481. doi: 10.11999/JEIT150995.
    HU Xiaowei, TONG Ningning, HE Xingyu, et al. High- resolution 3D imaging via wideband MIMO radar based on kronecker compressive sensing[J]. Journal of Electronics Information Technology, 2016, 38(6): 1475-1481. doi: 10. 11999/JEIT150995.
    MOHIMANI G H, BABAIE-ZADEH M, and JUTTEN C. A fast approach for overcomplete sparse decomposition based on smoothed l0 norm[J]. IEEE Transactions on Signal Processing, 2009, 57(1): 289-301. doi: 10.1109/TSP.2008. 2007606.
    齐焕芳, 徐源浩. 用于压缩感知信号重建的SL0改进算法[J]. 电子科技, 2015, 28(4): 27-30. doi: 10.16180/j.cnki.issn1007- 7820.2015.04.008.
    QI Huanfang and XU Yuanhao. Improved SL0 algorithm for compressive sensing signal reconstruction[J]. Electronic Science Technology, 2015, 28(4): 27-30. doi: 10.16180/ j.cnki.issn1007-7820.2015.04.008.
    CHANGZHENG M, TAT SOON Y, YONGBO Z, et al. MIMO radar 3D imaging based on combined amplitude and total variation cost function with sequential order one negative exponential form[J]. IEEE Transactions on Image Processing, 2014, 23(5): 2168-2183. doi: 10.1109/TIP.2014. 2311735.
    朱宇涛, 粟毅. 一种M2发N2收MIMO雷达平面阵列及其三维成像方法[J]. 中国科学: 信息科学, 2011, 41(12): 1495-1506.
    ZHU Yutao and SU Yi. A type of M2-transmitter N2-receiver MIMO radar array and 3D imaging theory[J]. Scientia Sinica (Informationis), 2011, 41(12): 1495-1506.
    赵瑞珍, 林婉娟, 李浩, 等. 基于光滑l0范数和修正牛顿法的压缩感知重建算法[J]. 计算机辅助设计与图形学学报, 2012, 24(4): 478-484.
    ZHAO Ruizhen, LIN Wanjuan, LI Hao, et al. Reconstruction algorithm for compressive sensing based on smoothed norm and revised newton method[J]. Journal of Compute-Aided Design Computer Graphics, 2012, 24(4): 478-484.
    HOU Biao, ZHANG Guang, LI Zhenwei, et al. Sparse coding-inspired high-resolution ISAR imaging using multistage compresive sensing[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(1): 26-40. doi: 10.1109/TAES.2017.2649161.
    ZUO Wangmei and LIN Zhouchen. A generalized accelerated proximal gradient approach for solving total-variation-based image restoration[J]. IEEE Transactions on Image Processing, 2011, 20(10): 2748-2759. doi: 10.1109/TIP.2011.2131665.
    乔田田. 基于优化模型和Bregman迭代的图像恢复算法研究[D]. [博士论文, 哈尔滨工业大学, 2014.
    QIAO Tiantian. Research on algorithms of image restoration based on l1 optimization model and Bregman iteration[D]. [Ph.D. dissertation], Harbin Institute of Technology, 2014.
  • 加载中
计量
  • 文章访问数:  1192
  • HTML全文浏览量:  122
  • PDF下载量:  259
  • 被引次数: 0
出版历程
  • 收稿日期:  2017-04-05
  • 修回日期:  2017-08-23
  • 刊出日期:  2017-12-19

目录

    /

    返回文章
    返回