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

基于原子范数最小化的步进频率 ISAR一维高分辨距离成像方法

吕明久 陈文峰 徐芳 赵欣 杨军

吕明久, 陈文峰, 徐芳, 赵欣, 杨军. 基于原子范数最小化的步进频率 ISAR一维高分辨距离成像方法[J]. 电子与信息学报, 2021, 43(8): 2267-2275. doi: 10.11999/JEIT200501
引用本文: 吕明久, 陈文峰, 徐芳, 赵欣, 杨军. 基于原子范数最小化的步进频率 ISAR一维高分辨距离成像方法[J]. 电子与信息学报, 2021, 43(8): 2267-2275. doi: 10.11999/JEIT200501
Mingjiu LÜ, Wenfeng CHEN, Fang XU, Xin ZHAO, Jun YANG. One Dimensional High Resolution Range Imaging Method of Stepped Frequency ISAR Based on Atomic Norm Minimization[J]. Journal of Electronics & Information Technology, 2021, 43(8): 2267-2275. doi: 10.11999/JEIT200501
Citation: Mingjiu LÜ, Wenfeng CHEN, Fang XU, Xin ZHAO, Jun YANG. One Dimensional High Resolution Range Imaging Method of Stepped Frequency ISAR Based on Atomic Norm Minimization[J]. Journal of Electronics & Information Technology, 2021, 43(8): 2267-2275. doi: 10.11999/JEIT200501

基于原子范数最小化的步进频率 ISAR一维高分辨距离成像方法

doi: 10.11999/JEIT200501
基金项目: 国家自然科学基金(61671469)
详细信息
    作者简介:

    吕明久:男,1985年生,讲师,主要研究方向为压缩感知在雷达成像中的应用

    陈文峰:男,1989年生,讲师,主要研究方向为双基地ISAR成像及压缩感知

    徐芳:女,1985年生,讲师,主要研究方向为研究计算机技术与运用

    赵欣:男 1981年生,讲师,主要研究方向为装备运用技术

    杨军:男,1973年生,教授,主要研究方向为雷达系统、雷达信号处理与检测理论

    通讯作者:

    吕明久 lv_mingjiu@163.com

  • 中图分类号: TN911.73; TN957.52

One Dimensional High Resolution Range Imaging Method of Stepped Frequency ISAR Based on Atomic Norm Minimization

Funds: The National Natural Science Foundation of China (61671469)
  • 摘要: 针对传统离散化压缩感知方法在网格失配条件下步进频率(SF) ISAR 1维距离成像估计性能下降的问题,该文提出一种基于原子范数最小化(ANM)的高分辨距离成像方法。首先,构建基于原子范数的无网格SF ISAR距离向稀疏表示模型,将1维距离成像问题转化为原子系数以及频率估计问题。然后,利用原子范数半正定性质,将原子范数最小化问题转化为半正定规划问题,并基于交替方向乘子法实现快速求解。最后,利用Vandermonde分解得到最终的1维高分辨距离成像结果。由于避免了网格离散化处理,因此可以实现网格失配、低量测值条件下的高分辨距离成像,且保持了高的距离分辨能力。理论分析与仿真实验验证了所提方法的有效性。
  • 图  1  基于原子范数的高分辨距离成像方法流程示意图

    图  2  不同算法1维距离像重构结果对比示意图

    图  3  不同条件下支撑集估计精度对比示意图

    图  4  不同算法重构结果对比示意图

    图  5  不同算法重构性能对比示意图

  • [1] WANG Lei, HUANG Tianyao, and LIU Yimin. Phase compensation and image autofocusing for randomized stepped frequency ISAR[J]. IEEE Sensors Journal, 2019, 19(10): 3784–3796. doi: 10.1109/JSEN.2019.2897014
    [2] 陈怡君, 李开明, 张群, 等. 稀疏线性调频步进信号ISAR成像观测矩阵自适应优化方法[J]. 电子与信息学报, 2018, 40(3): 509–516. doi: 10.11999/JEIT170554

    CHEN Yijun, LI Kaiming, ZHANG Qun, et al. Adaptive measurement matrix optimization for ISAR imaging with sparse frequency-stepped chirp signals[J]. Journal of Electronics &Information Technology, 2018, 40(3): 509–516. doi: 10.11999/JEIT170554
    [3] 龙腾, 丁泽刚, 肖枫, 等. 星载高分辨频率步进SAR成像技术[J]. 雷达学报, 2019, 8(6): 782–792. doi: 10.12000/JR19076

    LONG Teng, DING Zegang, XIAO Feng, et al. Spaceborne high-resolution stepped-frequency SAR imaging technology[J]. Journal of Radars, 2019, 8(6): 782–792. doi: 10.12000/JR19076
    [4] GHAFARI M, SABAHI M F, and ZHANG Zhenkai. Difference set coding in stepped frequency radar[C]. 6th Iranian Conference on Radar and Surveillance Systems, Isfahan, Iran, 2019: 1–6. doi: 10.1109/ICRSS48293.2019.9026562.
    [5] WEI Shaopeng, ZHANG Lei, MA Hui, et al. Sparse frequency waveform optimization for high-resolution ISAR Imaging[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(1): 546–566. doi: 10.1109/TGRS.2019.2937965
    [6] GANGULY S, GHOSH I, RANJAN R, et al. Compressive sensing based off-grid DOA estimation using OMP algorithm[C]. The 6th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, 2019. doi: 10.1109/SPIN.2019.8711677.
    [7] HU Lei, SHI Zhiguang, ZHOU Jianxiong, et al. Compressed sensing of complex sinusoids: An approach based on dictionary refinement[J]. IEEE Transactions on Signal Processing, 2012, 60(7): 3809–3822. doi: 10.1109/TSP.2012.2193392
    [8] 王伟, 胡子英, 龚琳舒. MIMO雷达三维成像自适应Off-grid校正方法[J]. 电子与信息学报, 2019, 41(6): 1294–1301. doi: 10.11999/JEIT180145

    WANG Wei, HU Ziying, and GONG Linshu. Adaptive off-grid calibration method for MIMO radar 3D imaging[J]. Journal of Electronics &Information Technology, 2019, 41(6): 1294–1301. doi: 10.11999/JEIT180145
    [9] EKANADHAM C, TRANCHINA D, and SIMONCELLI E P. Recovery of sparse translation-invariant signals with continuous basis pursuit[J]. IEEE Transactions on Signal Processing, 2011, 59(10): 4735–4744. doi: 10.1109/TSP.2011.2160058
    [10] HUANG Limei, ZONG Zhulin, HUANG Libing, et al. Off-grid sparse stepped-frequency SAR imaging with adaptive basis[C]. 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019: 2925–2928. doi: 10.1109/IGARSS.2019.8898543.
    [11] YANG Zai, XIE Lihua, and ZHANG Cishen. Off-grid direction of arrival estimation using sparse Bayesian inference[J]. IEEE Transactions on Signal Processing, 2013, 61(1): 38–43. doi: 10.1109/TSP.2012.2222378
    [12] TANG Gongguo, BHASKAR B N, SHAH P, et al. Compressed sensing off the grid[J]. IEEE Transactions on Information Theory, 2013, 59(11): 7465–7490. doi: 10.1109/TIT.2013.2277451
    [13] YANG Zai and XIE Lihua. Continuous compressed sensing with a single or multiple measurement vectors[C]. 2014 IEEE Workshop on Statistical Signal Processing, Gold Coast, Australia, 2014: 288–291. doi: 10.1109/SSP.2014.6884632.
    [14] CHANDRASEKARAN V, RECHT B, PARRILO P A, et al. The convex algebraic geometry of linear inverse problems[C]. The 48th Annual Allerton Conference on Communication, Control, and Computing, Allerton, USA, 2010: 699–703. doi: 10.1109/ALLERTON.2010.5706975.
    [15] 吕明久, 陈文峰, 夏塞强, 等. 基于联合块稀疏模型的随机调频步进ISAR成像方法[J]. 电子与信息学报, 2018, 40(11): 2614–2620. doi: 10.11999/JEIT180054

    LÜ Mingjiu, CHEN Wenfeng, XIA Saiqiang, et al. Random chirp frequency-stepped signal ISAR imaging algorithm based on joint block-sparse model[J]. Journal of Electronics &Information Technology, 2018, 40(11): 2614–2620. doi: 10.11999/JEIT180054
    [16] BHASKAR B N, TANG Gongguo, and RECHT B. Atomic norm denoising with applications to line spectral estimation[J]. IEEE Transactions on Signal Processing, 2013, 61(23): 5987–5999. doi: 10.1109/TSP.2013.2273443
    [17] HANSEN T L and JENSEN T L. A fast interior-point method for atomic norm soft thresholding[J]. Signal Processing, 2019, 165: 7–19. doi: 10.1016/j.sigpro.2019.06.023
    [18] BOYD S, PARIKH N, CHU E, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers[J]. Foundations and Trends® in Machine Learning, 2011, 3(1): 1–122. doi: 10.1561/2200000016
    [19] GEORGIOU T T. The Carathéodory-Fejér-Pisarenko decomposition and its multivariable counterpart[J]. IEEE Transactions on Automatic Control, 2007, 52(2): 212–228. doi: 10.1109/TAC.2006.890479
    [20] YANG Zai and XIE Lihua. On gridless sparse methods for line spectral estimation from complete and incomplete data[J]. IEEE Transactions on Signal Processing, 2015, 63(12): 3139–3153. doi: 10.1109/tsp.2015.2420541
    [21] ZHANG Zhe, WANG Yue, and TIAN Zhi. Efficient two-dimensional line spectrum estimation based on decoupled atomic norm minimization[J]. Signal Processing, 2019, 163: 95–106. doi: 10.1016/j.sigpro.2019.04.024
    [22] LI Yinchuan, WANG Xiaodong, and DING Zegang. Multi-dimensional spectral super-resolution with prior knowledge via frequency-selective Vandermonde decomposition and ADMM[EB/OL]. https://arxiv.org/abs/1906.00278, 2019.
  • 加载中
图(5)
计量
  • 文章访问数:  1047
  • HTML全文浏览量:  543
  • PDF下载量:  96
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-06-08
  • 修回日期:  2020-11-10
  • 网络出版日期:  2020-12-10
  • 刊出日期:  2021-08-10

目录

    /

    返回文章
    返回