短脉冲非相参雷达的逆合成孔径成像及其稀疏恢复成像技术

汪海波; 黄文华; 巴涛; 姜悦

doi:10.11999/JEIT180912

短脉冲非相参雷达的逆合成孔径成像及其稀疏恢复成像技术

doi: 10.11999/JEIT180912

西北核技术研究所高功率微波技术重点实验室西安 710024

详细信息

作者简介:
汪海波：男，1987年生，工程师，研究方向为高功率微波技术和雷达信号处理

黄文华：男，1968年生，研究员，博士生导师，研究方向为高功率微波技术

姜悦：女，1989年生，工程师，研究方向为高功率微波技术、特征提取与目标识别

通讯作者:
汪海波　wanghaibo@nint.ac.cn

中图分类号: TN951
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出版历程
- 收稿日期: 2018-09-21
- 修回日期: 2019-01-12
- 网络出版日期: 2019-05-20
- 刊出日期: 2019-11-01

Inverse Synthetic Aperture Radar Imaging with Non-Coherent Short Pulse Radar and Its Sparse Recovery

Science and Technology on High Power Microwave Laboratory, Northwest Institute of Nuclear Technology, Xi’an 710024, China

摘要

摘要: 短脉冲非相参雷达(NCSP)的辐射源输出微波脉冲持续时间短，针对于高速运动目标而言，其脉冲持续时间内的目标运动可忽略不计，对回波信号不需进行专门的脉冲内运动补偿。为了利用短脉冲非相参雷达信号进行逆合成孔径雷达成像，该文应用补偿相参处理的方法，去除辐射信号包络时间不确定性和初始相位的不确定性影响，在常规方法进行包络对齐和初相补偿后可利用距离-多普勒(RD)方法进行逆合成孔径雷达成像，仿真验证了补偿后信号成像的可行性。然而，短脉冲非相参雷达的载频随机抖动的因素会导致距离-多普勒成像结果在多普勒维度产生随机调制的旁瓣，影响成像的质量。利用稀疏恢复技术，在成像空间中对目标的散射中心进行稀疏重构，利用正交匹配追踪(OMP)算法和稀疏贝叶斯学习(SBL)算法进行成像，从而实现了抑制非相参因素引起的成像旁瓣，改进了成像质量，通过仿真验证了方法可行性。
- 短脉冲非相参雷达 /
- 逆合成孔径成像 /
- 稀疏恢复
Abstract: The microwave source of Non-Coherent Short Pulse (NCSP) radar transmits short pulse. Thus, for high velocity targets, the motion effect in the pulse duration can be neglected, and the echo signal does not need special motion compensation. In order to use the NCSP radar signal for Inverse Synthetic Aperture Radar (ISAR) imaging, the compensation coherent processing method is applied to removing the uncertainty of the envelope time and the initial phase uncertainty. Assuming that the echo is envelope-aligned and initially compensated by conventional methods, ISAR radar imaging can be performed using the Range-Doppler (RD) method, subsequently. The simulation verifies the feasibility of the compensation signal ISAR imaging. However, the carrier-frequency random jitter factor of NCSP radar causes random-modulated sidelobes in the Doppler dimension, which affect imaging quality. In this paper, the sparse recovery technique is used to perform sparse reconstruction of the target scattering center in the imaging space. The Orthogonal Matching Pursuit (OMP) algorithm and the Sparse Bayesian Learning (SBL) algorithm are used as the recovery algorithm for imaging simulation experiments. The simulation results show that the sparse recovery technique can suppress the imaging sidelobes caused by non-coherence and improve the imaging quality.
- Non-Coherent Short Pulse (NCSP) radar /
- Inverse Synthetic Aperture radar (ISAR) /
- Sparse recovery

HTML全文

图 1 短脉冲非相参雷达系统结构

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图 2 3种信号对运动目标回波的对比

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图 3 成像几何模型

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图 4 点目标模型的设定

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图 5 频率抖动示意

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图 6 RD成像结果

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图 7 稀疏恢复成像结果

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参考文献(32)

胡银福, 冯进军. 用于雷达的新型真空电子器件[J]. 雷达学报, 2016, 5(4): 350–360. doi: 10.12000/JR16078

HU Yinfu and FENG Jinjun. New vacuum electronic devices for radar[J]. Journal of Radars, 2016, 5(4): 350–360. doi: 10.12000/JR16078

钱宝良. 国外高功率微波技术的研究现状与发展趋势[J]. 真空电子技术, 2015(2): 1–74. doi: 10.16540/j.cnki.cn11-2485/tn.2015.02.001

QIAN Baoliang. The research status and developing tendency of high power microwave technology in foreign countries[J]. Vacuum Electronics, 2015(2): 1–74. doi: 10.16540/j.cnki.cn11-2485/tn.2015.02.001

XIAO Renzhen, ZHANG Zhiqiang, LIANG Tiezhu, et al. A relativistic backward wave oscillator for directly generating circularly polarized TE11 mode[J]. Physics of Plasmas, 2016, 23(3): 033118. doi: 10.1063/1.4944915

BLYAKHMAN A, CLUNIE D, HARRIS R, et al. Nanosecond gigawatt radar: Indication of small targets moving among heavy clutters[C]. 2007 IEEE Radar Conference, Boston, USA, 2007: 61–64. doi: 10.1109/RADAR.2007.374191.

BLYAKHMAN A, CLUNIE D, MESIATS G, et al. Analysis of Nanosecond Gigawatt Radar[M]. Hirshfield J L and Petelin M L. Quasi-Optical Control of Intense Microwave Transmission. Netherlands: Springer, 2005: 283–296. doi: 10.1007/1-4020-3638-8_21.

AUBRY A, DE MAIO A, CAROTENUTO V, et al. Radar phase noise modeling and effects-Part I: MTI filters[J]. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52(2): 698–711. doi: 10.1109/TAES.2015.140549

AUBRY A, CAROTENUTO V, DE MAIO A, et al. Radar phase noise modeling and effects-Part II: doppler processors and sidelobe blankers[J]. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52(2): 712–725. doi: 10.1109/TAES.2015.140723

RYSKIN N M, TITOV V N, and UMANTSIVA O V. Phase locking and mode switching in a backward-wave oscillator with reflections[J]. IEEE Transactions on Plasma Science, 2016, 44(8): 1270–1275. doi: 10.1109/TPS.2016.2517002

SONG Wei, SUN Jun, SHAO Hao, et al. Inducing phase locking of multiple oscillators beyond the Adler’s condition[J]. Journal of Applied Physics, 2012, 111(2): 023302. doi: 10.1063/1.3671537

SONG Wei, ZHANG Xiaowei, CHEN Changhua, et al. Enhancing frequency-tuning ability of an improved relativistic backward-wave oscillator[J]. IEEE Transactions on Electron Devices, 2013, 60(1): 494–497. doi: 10.1109/TED.2012.2230400

XIAO Renzhen, SONG Zhimin, DENG Yuqun, et al. Mechanism of phase control in a klystron-like relativistic backward wave oscillator by an input signal[J]. Physics of Plasmas, 2014, 21(9): 093108. doi: 10.1063/1.4895598

汪海波, 黄文华, 姜悦. 短脉冲非相参雷达的补偿相参处理方法研究[J]. 电子与信息学报, 2018, 40(8): 1823–1828. doi: 10.11999/JEIT171147

WANG Haibo, HUANG Wenhua, and JIANG Yue. Compensative coherent processing algorithm for short pulse non-coherent radar[J]. Journal of Electronics &Information Technology, 2018, 40(8): 1823–1828. doi: 10.11999/JEIT171147

PRICKETT M J. Principles of inverse synthetic aperture radar (ISAR)[J]. IEEE EASCON Record, 1980, 14(6): 340–345.

ZHANG Lei, QIAO Zhijun, XING Mengdao, et al. High-resolution ISAR imaging with sparse stepped-frequency waveforms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(11): 4630–4651. doi: 10.1109/TGRS.2011.2151865

KHARDIKOV V V and PROSVIRNIN S L. The Algorithm for ISAR imaging of fast moving target using radar with binary phase–coded waveforms[C]. The 5th International Conference on Antenna Theory and Techniques, Kyiv, Ukraine, 2005: 339–342. doi: 10.1109/ICATT.2005.1496974.

白雪茹. 空天目标逆合成孔径雷达成像新方法研究[D]. [博士论文], 西安电子科技大学, 2011.

BAI Xueru. Study on new techniques for ISAR imaging of aerospace targets[D]. [Ph.D. dissertation], Xidian University, 2011.

RAO Wei, LI Gang, and WANG Xiqin. Parametric sparse representation method for ISAR imaging of rotating targets[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(2): 910–919. doi: 10.1109/TAES.2014.120535

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.J.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 &Information Technology, 2014, 36(1): 187–193. doi: 10.3724/SP.J.1146.2012.01597

ZHANG Lei, QIAO Zhijun, XING Mengdao, et al. High-resolution ISAR imaging by exploiting sparse apertures[J]. IEEE Transactions on Antennas and Propagation, 2012, 60(2): 997–1008. doi: 10.1109/TAP.2011.2173130

李少东, 杨军, 陈文峰, 等. 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

LI Shaodong, YANG Jun, CHEN Wenfeng, et al. 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

HE Xingyu, TONG Ningning, HU Xiaowei, et al. High-resolution ISAR imaging based on two-dimensional group sparse recovery[J]. IET Radar, Sonar & Navigation, 2018, 12(1): 82–86. doi: 10.1049/iet-rsn.2017.0161

LIU Zhen, CHEN Xin, and SUI Jinping. High resolution wideband imaging of fast rotating targets based on random PRI radar[J]. Progress in Electromagnetic Research M, 2018, 63: 59–70. doi: 10.2528/PIERM17081005

TROPP J A and GILBERT A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2007, 53(12): 4655–4666. doi: 10.1109/TIT.2007.909108

吕明久, 李少东, 杨军, 等. 基于随机调频步进信号的高分辨ISAR成像方法[J]. 电子与信息学报, 2016, 38(12): 3129–3136. doi: 10.11999/JEIT160177

LÜ Mingjiu, LI Shaodong, YANG Jun, et al. High resolution ISAR imaging method based on random chirp frequency-stepped signal[J]. Journal of Electronics &Information Technology, 2016, 38(12): 3129–3136. doi: 10.11999/JEIT160177

LIU Hongchao, JIU Bo, LIU Hongwei, et al. Superresolution ISAR imaging based on sparse Bayesian learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(8): 5005–5013. doi: 10.1109/TGRS.2013.2286402

JI Shihao, XUE Ya, and CARIN L. Bayesian compressive sensing[J]. IEEE Transactions on Signal Processing, 2008, 56(6): 2346–2356. doi: 10.1109/TSP.2007.914345

BABACAN S D, MOLINA R, and KATSAGGELOS A K. Fast Bayesian compressive sensing using Laplace priors[C]. IEEE International Conference on Acoustics, Speech and Signal Processing, Taipei, China, 2009: 2873–2876. doi: 10.1109/ICASSP.2009.4960223.

苏伍各, 王宏强, 邓彬, 等. 基于稀疏贝叶斯方法的脉间捷变频ISAR成像技术研究[J]. 电子与信息学报, 2015, 37(1): 1–8. doi: 10.11999/JEIT140315

SU Wuge, WANG Hongqiang, DENG Bin, et al. The interpulse frequency agility ISAR imaging technology based on sparse Bayesian method[J]. Journal of Electronics &Information Technology, 2015, 37(1): 1–8. doi: 10.11999/JEIT140315

吴顺君, 梅晓春. 雷达信号处理和数据处理技术[M]. 北京: 电子工程出版社, 2008: 51–80.

WU Shunjun and MEI Xiaochun. Radar Signal Processing and Information Processing Technology[M]. Beijing: Publishing House of Electronics Industry, 2008: 51–80.

POTTER L C, CHIANG Daming, CARRIERE R, et al. A GTD-based parametric model for radar scattering[J]. IEEE Transactions on Antennas and Propagation, 1995, 43(10): 1058–1067. doi: 10.1109/8.467641

O'DONNELL A N, WILSON J L, KOLTENUK D M, et al. Compressed sensing for radar signature analysis[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(4): 2631–2639. doi: 10.1109/TAES.2013.6621841

李峰, 郭毅. 压缩感知浅析[M]. 北京: 科学出版社, 2015: 45–79.

LI Feng and GUO Yi. Introduce to Compress Sensing[M]. Beijing: Science Press, 2015: 45–79.

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