低信噪比下二维联合快速超分辨B-ISAR成像方法

doi:10.3969/j.issn.0372-2112.2018.04.011

电子学报 ›› 2018, Vol. 46 ›› Issue (4): 840-848.DOI: 10.3969/j.issn.0372-2112.2018.04.011

低信噪比下二维联合快速超分辨B-ISAR成像方法

陈文峰¹, 李少东², 杨军¹, 马晓岩¹

1. 空军预警学院, 湖北武汉 430019;
2. 解放军93253部队, 辽宁大连 116000

收稿日期:2016-11-07 修回日期:2017-03-22 出版日期:2018-04-25
- 作者简介:
- 陈文峰 男,1989年9月出生,新疆伊犁人.2012年和2014年在空军预警学院分别获工学学士和工学硕士学位.现为空军预警学院博士生,从事双基地逆合成孔径雷达成像及压缩感知方面的研究.E-mail:chenwf925@163.com;李少东 男,1987年9月出生,河北保定人.2010年、2012年和2016年在空军预警学院分别获工学学士、工学硕士和工学博士学位.现为解放军93253部队工程师,从事逆合成孔径雷达成像及压缩感知方面的研究
- 基金资助:
- 国家自然科学基金 (No.61671469）

A Fast Two Dimensional Joint Super-Resolution B-ISAR Imaging Algorithm Under Low SNR

CHEN Wen-feng¹, LI Shao-dong², YANG Jun¹, MA Xiao-yan¹

1. Air Force Early Warning Academy, Wuhan, Hubei 430019, China;
2. The Unit 93253 of PLA, Dalian, Liaoning 116000, China

Received:2016-11-07 Revised:2017-03-22 Online:2018-04-25 Published:2018-04-25
- Supported by:
- National Natural Science Foundation of China (No.61671469)

摘要/Abstract

摘要： 双基地ISAR成像分辨率受限于信号带宽和方位积累时间，且成像质量受噪声影响严重.本文在充分考虑回波的二维联合稀疏特征基础上，提出二维联合字典下的矩阵复数近似消息传递超分辨快速成像算法.在构建双基地ISAR的二维联合超分辨成像模型基础上，首先通过向量化处理，将二维超分辨成像问题转换为复数基追踪去噪问题；其次通过两种策略实现复数基追踪去噪问题的快速求解，一是利用向量化与Kronecker积的关系，推导出矩阵形式复数近似消息传递算法，从而避免向量化处理带来的大矩阵运算量和大存储量问题；二是为进一步减少单次迭代的运算量，将矩阵乘法等效为二维快速傅里叶变换.最后，利用本文算法在迭代过程中对噪声阈值不断精确逼近，提高算法在低信噪比下的成像能力.仿真数据成像结果验证了本文算法的有效性.

关键词: 双基地逆合成孔径雷达, 二维超分辨成像, 复数近似消息传递, 压缩感知

Abstract:

In B-ISAR imaging, the range resolution and cross-range resolution are dependent on the signal band and the coherent processing interval, respectively. Generally, the B-ISAR image is seriously affected by noise. In this paper, a matrix form of complex approximate message passing algorithm based on two dimensional coupled dictionaries (MCAMP-TCD) is presented, by considering the 2D coupling sparse feature of the echo. Firstly, the range-azimuth 2D joint B-ISAR imaging model is established. Then the 2D joint super-resolution imaging problem is converted into a complex basis pursuit denoising (C-BPDN) problem through vectorization operation. Secondly, two strategies are implemented to solve C-BPDN problem quickly, the first strategy is utilizing the relation between vectorization operation and Kronecker product to derivate the matrix form of complex approximate message passing algorithm, which can avoid the high computational complexity and memory requirements due to vectorization operation. In second strategy, the two dimensional fast Fourier transform (2D FFT) is introduced to equivalent matrix multiplication, which further reduces the computational complexity of the single iteration. At last, the imaging capability under low signal to noise ratio (SNR) is improved by the ability to accurately approximate the noise threshold of the MCAMP-TCD. Simulation results verify the effectiveness of the proposed method.

Key words: bistatic inverse synthetic aperture radar (B-ISAR), 2D super-resolution imaging, complex approximate message passing, compressive sensing

中图分类号:

TN957.52

陈文峰, 李少东, 杨军, 等. 低信噪比下二维联合快速超分辨B-ISAR成像方法[J]. 电子学报, 2018, 46(4): 840-848.

CHEN Wen-feng, LI Shao-dong, YANG Jun, et al. A Fast Two Dimensional Joint Super-Resolution B-ISAR Imaging Algorithm Under Low SNR[J]. Acta Electronica Sinica, 2018, 46(4): 840-848.

参考文献

[1] 杨振起,张永顺,骆永军.双(多)基地雷达系统[M].北京:国防工业出版社,1998. Yang Zhen-qi,ZHANG Yong-shun,LUO Yong-jun.Bistatic (Multistatic) Radar System[M].Beijing:Defense Industry Press,1998.(in Chinese)
[2] CHEN V C,ROSIERS A D,LIPPS R.Bi-static ISAR range-Doppler imaging and resolution analysis[A].2009 IEEE Radar Conference[C].California:Pasadena,2009.1-5.
[3] 艾小峰,邹小海,李浩智,等.T/R-R双基地雷达进动目标参数估计与ISAR成像[J].电子学报,2012,40(6):1148-1153. AI Xiao-feng,ZOU Xiao-hai,LI Hao-zhi,et al.Parameter estimation and ISAR imaging of precession targets using T/R-R bistatic radars[J].Acta Electronica Sinica,2012,40(6):1148-1153.(in Chinese)
[4] ZHANG Shun-sheng,SUN Si-bo,ZHANG Wei,et al.High-resolution bistatic ISAR image formation for high-speed and complex-motion targets[J].IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing,2015,8(7):3520-3531.
[5] 郭宝锋,尚朝轩,王俊岭,等.双基地角时变下的逆合成孔径雷达越分辨单元徙动校正算法[J].物理学报,2014,63(23):238406. Guo Bao-feng,Shang Chao-xuan,Wang Jun-ling,et al.Correction of migration through resolution cell in bistatic inverse synthetic aperture radar in the presence of time-varying bistatic angle[J].Acta Physica Sinica,2014,63(23):238406.(in Chinese)
[6] 张龙,苏涛,刘峥,等.双基地雷达两极区ISAR超分辨成像[J].光子学报,2015,44(3):0328002. ZHANG Long,SU Tao,LIU Zheng,et al.Super resolution ISAR imaging in receiver centered region area in bistatic[J].Acta Photonica Sinica,2015,44(3):0328002.(in Chinese)
[7] 柴守刚,陈卫东,陈畅.联合几何畸变校正及定标的B-ISAR稀疏成像算法[J].现代雷达,2015,32(1):32-37. CHAI Shou-gang,CHEN Wei-dong,CHEN Chang.B-ISAR sparse imaging algorithm with geometric distortion correction and calibration[J].Modern Radar,2015,32(1):32-37.(in Chinese)
[8] ZHANG Shun-sheng,ZHANG Wei,ZONG Zhu-lin,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.
[9] 李少东,陈文峰,杨军,等.低信噪比下的二维联合线性布雷格曼迭代快速超分辨成像算法[J].物理学报,2016,65(3):038401. LI Shao-dong,CHEN Wen-feng,YANG Jun,et al.A fast two dimensional joint linearized bregman iteration algorithm for super-resolution inverse synthetic aperture radar imaging at low signal-to-noise ratios[J].Acta Physica Sinica,2016,65(3):038401.(in Chinese)
[10] 韩宁,尚朝轩,董健,等.小转角下空间目标双基地ISAR二维成像算法[J].传感器与微系统,2011,30(11):138-141. HAN Ning,SHANG Chao-xuan,DONG Jian,et al.2D imaging algorithm for bistatic ISAR of space target in small rotating angle[J].Transducer and Microsystem Technologies,2011,30(11):138-141.(in Chinese)
[11] DONOHO D L.Compressed sensing[J].IEEE Transactions on Information Theory,2006,52(4):1289-1306.
[12] 韩宁,刘伟,何强,等.稀疏正则化在双基地ISAR距离成像中的应用[J].现代雷达,2012,34(4):24-27. HAN Ning,LIU Wei,HE Qiang,et al.Application of sparse regularization in bistatic ISAR range profile imaging[J].Modern Radar,2012,34(4):24-27.(in Chinese)
[13] LIU Zhen,WEI Xi-zhang,LI Xiang.Decoupled ISAR imaging using RSFW based on twice compressed sensing[J].IEEE Transactions on Aerospace and Electronic Systems,2014,50(4):3195-3211.
[14] 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. WU Min,XING Meng-dao,ZHANG Lei.Two dimensional joint super-resolution ISAR imaging algorithm based on compressive sensing[J].Journal of Electronics & Information Technology,2014,36(1):187-193.(in Chinese)
[15] 张龙,张磊,邢孟道.一种基于改进压缩感知的低信噪比ISAR高分辨成像方法[J].电子与信息学报,2010,32(9):2263-2267. ZHANG Long,ZHANG Lei,XING Meng-dao.A new method of high resolution ISAR imaging under low SNR based on improved compressive sensing[J].Journal of Electronics & Information Technology,2010,32(9):2263-2267.(in Chinese)
[16] 李少东,陈文峰,杨军,等.任意稀疏结构的多量测向量快速稀疏重构方法研究[J].电子学报,2015,43(4):708-715. LI Shao-dong,CHEN Wen-feng,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.(in Chinese)
[17] 王天云,陆新飞,丁丽,等.基于贝叶斯压缩感知的FD-MIMO雷达OFF-Grid目标稀疏成像[J].电子学报,2016,44(6):1314-1321. WANG Tian-yun,LU Xin-fei,DING Li,et al.Bayesian compressive sensing-based sparse imaging for off-grid target in frequency diverse MIMO radar[J].Acta Electronica Sinica,2016,44(6):1314-1321.(in Chinese)
[18] MALEKI A,et al.Asymptotic analysis of complex LASSO via complex approximate message passing[J].IEEE Transactions on Information Theory,2013,59(7):4290-4308.
[19] 朱仁飞,罗迎,张群,等.双基地ISAR成像分析[J].现代雷达,2011,33(8):33-38. ZHU Ren-fei,LUO Ying,ZHANG Qun,et al.Analysis of bistatic inverse synthetic aperture radar imaging[J].Modern Radar,2011,33(8):33-38.(in Chinese)
[20] 张贤达.矩阵分析与应用[M].北京:清华大学出版社,2013. ZHANG Xian-da.Matrix Analysis and Applications[M].Beijing:Tsinghua University Press,2013.(in Chinese)
[21] MALEKI A.Approximate message passing algorithm for compressed sensing[D].Stanford University,2011.
[22] DONOHO D L,MALEKI A,MONTANARI A.Message passing algorithms for compressed sensing[J].Proceedings of the National Academy of the Sciences of the United States of America,2009,106(45):18914-18919.
[23] YANG J,ZHANG Y.Alternating direction algorithms for L1-problems in compressive sensing[J].SIAM Journal on Scientific Computing,2011,33(1):250-278.
[24] 张磊.高分辨SAR/ISAR成像及误差补偿技术研究[D].西安:西安电子科技大学,2012. ZHANG Lei.Study on high resolution SAR/ISAR imaging and error correction[D].Xi'an:Xidian University,2012.(in Chinese)

低信噪比下二维联合快速超分辨B-ISAR成像方法

A Fast Two Dimensional Joint Super-Resolution B-ISAR Imaging Algorithm Under Low SNR

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	陈胜垚, 席峰, 张素玲, 刘中. 正交压缩采样系统时域分析[J]. 电子学报, 2023, 51(3): 573-584.
[2]	吕明久, 马建朝, 韦旭, 陈文峰, 杨军, 马晓岩. 基于矩阵化原子范数的高频雷达距离-多普勒二维估计方法[J]. 电子学报, 2022, 50(5): 1150-1158.
[3]	苏林林, 陈亮, 陈菲菲, 周鑫, 焦振航, 刘钊良. 面向B5G/6G的GFDM信号高精度测距与定位研究[J]. 电子学报, 2022, 50(4): 849-859.
[4]	魏志超, 杨春玲. 时域注意力特征对齐的视频压缩感知重构网络[J]. 电子学报, 2022, 50(11): 2584-2592.
[5]	陈文俊, 杨春玲. 图像压缩感知的特征域优化及自注意力增强神经网络重构算法[J]. 电子学报, 2022, 50(11): 2629-2637.
[6]	左婷, 王法松, 张建康, 李睿. 室内可见光通信系统中基于压缩感知的空移键控信号检测方法[J]. 电子学报, 2022, 50(1): 36-44.
[7]	冯西安, 寇思玮, 谭伟杰, 毕杨. 水声信号处理中的稀疏表示理论及应用[J]. 电子学报, 2021, 49(9): 1840-1851.
[8]	裴翰奇, 杨春玲, 魏志超, 曹燕. 基于SPL迭代思想的图像压缩感知重构神经网络[J]. 电子学报, 2021, 49(6): 1195-1203.
[9]	禤韵怡, 杨春玲. 基于帧间组稀疏的两阶段递归增强视频压缩感知重构网络[J]. 电子学报, 2021, 49(3): 435-442.
[10]	李峰, 詹邦成, 辛蕾, 刘洋, 刘志佳, 肖化超. 基于新型联合感知矩阵的压缩学习目标识别技术[J]. 电子学报, 2021, 49(11): 2108-2116.
[11]	郑恩明, 陈新华, 周权斌, 李嶷, 杨鹤, 孟浩. 一种复域压缩感知目标方位估计方法[J]. 电子学报, 2021, 49(11): 2117-2123.
[12]	孙泽宇, 李传锋, 阎奔. 传感网中基于压缩感知的丢包匹配数据收集算法[J]. 电子学报, 2020, 48(4): 723-733.
[13]	陈兵兵, 曹欣远, 陈明生, 齐琦, 况晓静, 吴先良, 陈辉. 基于欠定方程的频谱峰值搜索算法及其在连续毫米波雷达系统中的应用[J]. 电子学报, 2020, 48(2): 369-374.
[14]	郑学炜, 杨春玲, 禤韵怡. CVS中基于视频运动特征的多假设-双稀疏重构算法[J]. 电子学报, 2020, 48(2): 249-257.
[15]	马彬, 王宏明, 谢显中. 基于二项分布改进的宽带压缩频谱检测方案[J]. 电子学报, 2020, 48(2): 243-248.