High Resolution ISAR Imaging Method Based on Random Chirp Frequency-stepped Signal

Lü Mingjiu; LI Shaodong; YANG Jun; MA Xiaoyan

doi:10.11999/JEIT160177

Volume 38 Issue 12

Jan. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(12): 3129-3136

Lü Mingjiu, LI Shaodong, YANG Jun, MA Xiaoyan. 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

Citation:

Lü Mingjiu, LI Shaodong, YANG Jun, MA Xiaoyan. 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

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High Resolution ISAR Imaging Method Based on Random Chirp Frequency-stepped Signal

doi: 10.11999/JEIT160177

Funds:

The National Natural Science Foundation of China (61671469)

Received Date: 2016-03-01
Rev Recd Date: 2016-09-01
Publish Date: 2016-12-19

Abstract

Abstract

In order to make full use of the joint sparse physical characteristics of the radar echo to improve imaging performance. A novel super resolution Inverse SAR (ISAR) imaging method based on distributed compressed sensing theory is proposed. Firstly, the joint sparse echo model of the random chirp frequency-stepped signal is built and the pulse compression processing of each sub-pulse is processed. Secondly, owing to different random patterns of each group, different measurement matrices are constructed in accordance with the random pattern of sub-pulse signal. Then the corresponding compressed sensing model of the echo is built and the supper resolution range profile is obtained via the distributed compressed sensing theory. Finally, the supper resolution inverse synthetic aperture radar image can be obtained by a fast compressed sensing reconstruction algorithm, which is used to achieve the high resolution reconstruction in azimuth direction based on the sparse features. Theoretical analysis and simulation results show that the proposed method has the characteristics of high reconstruction accuracy, low sampling rate and strong anti-noise performance.
- Inverse SAR (ISAR) imaging,
- Joint sparsity model,
- Distributed compressed sensing,
- Random chirp frequency-stepped signal

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References(32)

References

DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.

JIU Bo, LIU Hongchao, Liu Hongwei, et al. Joint ISAR imaging and cross-range scaling method based on compressive sensing with adaptive dictionary[J]. IEEE Transactions on Antennas Propagation, 2015, 63(5): 2112-2121. doi: 10.1109/TAP.2015.2409876.

田野, 张冰尘, 洪文. 基于分布式压缩感知的重轨干涉SAR形变检测方法与实验[J]. 中国科学院大学学报, 2016, 33(1): 107-114. doi: 10.7523/j.issn.2095-6134.2016.01.016.

TIAN Ye, ZHANG Bingchen, and HONG Wen. Deformation detection approach for repeat-pass InSAR based on distributed compressed sensing[J]. Journal of University of Chinese Academy of Sciences, 2016, 33(1): 107-114. doi: 10.7523/j.issn.2095-6134.2016.01.016.

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算法研究[J]. 电子学报, 2015, 43(4): 708-715. doi: 10.396/j.issn.0372-2112.2015.04.012.

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. doi: 10.3969/j.issn.0372-2112. 2015.04.012.

ZHANG Shunsheng, ZHANG Wei, ZONG Zhulin, et al. High-resolution bistatic ISAR imaging based on two-dimensional compressed sensing[J]. IEEE Transactions on Antennas Propagation, 2015, 63(5): 2098-2111. doi: 10.1109/TAP.2015.2408337.

ERTIN E. Frequency diverse waveforms for compressive radar sensing[C]. Waveform Diversity Design Conference, Niagara Falls, Ontario, Canada, 2010: 216-219. doi: 10.1109/ WDD.2010.5592522.

孙延鹏, 王艺霖, 屈乐乐. 基于贝叶斯压缩感知的频率步进探地雷达成像算法[J]. 沈阳航空航天大学学报, 2015, 32(5): 68-73. doi: 10.3969/j.issn.2095-1248.2015.05.010.

SUN Yanpeng, WANG Yilin, and QU Lele. Stepped- frequency GPR imaging algorithm based on Bayesian compressive sensing[J]. Journal of Shenyang Aerospace University, 2015, 32(5): 68-73. doi: 10.3969/j.issn.2095-1248. 2015.05.010.

邵鹏, 邢孟道, 李学仕, 等. 一种新的频域带宽合成的斜视高分辨SAR成像方法[J]. 西安电子科技大学学报, 2015, 42(2): 28-34. doi: 10.3969/j.issn.1001-2400.2015.02.005.

SHAO Peng, XING Mengdao, LI Xueshi, et al. Squinted high resolution SAR based on the frequency synthetic bandwidth[J]. Journal of Xidian University, 2015, 42(4): 28-34. doi: 10.3969/j.issn.1001-2400.2015.02.005.

SEYFRIED D and SCHOEBEL J. Stepped-frequency radar signal processing[J]. Journal of Applied Geophysics, 2015, 112: 42-51.

王虹现, 梁毅, 邢孟道, 等. 基于稀疏线性调频步进信号的ISAR成像[J]. 中国科学：信息科学, 2011, 41(12): 1529-1540. doi: 1007/s11432-011-4353-1.

WANG Hongxian, LIANG Yi, XING Mengdao, et al. ISAR imaging via sparse frequency-stepped chirp signal[J]. SCIENTIA SINICA Informationis, 2011, 41(12): 1529-1540. doi: 1007/s11432-011-4353-1.

ZHU Feng, ZHANG Qun, LUO Ying, et al. A novel cognitive ISAR imaging method with random stepped frequency chirp signal[J]. SCIECE CHINA Information Sciences, 2012, 55(8): 19101924. doi: 10.1007/s11432-012-4629-0.

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.

张榆红, 邢孟道, 徐刚. 基于稀疏孔径的联合稀疏约束干涉ISAR机动目标三维成像[J]. 电子与信息学报, 2015, 37(9): 2151-2157. doi: 10.11999/JEIT150125.

ZHANG Yuhong, XING Mengdao, and XU Gang. Joint sparsity constraint interferometric ISAR Imaging for 3-D geometry of maneuvering targets with sparse apertures[J]. Journal of Electronics Information Technology, 2015, 37(9): 2151-2157. doi: 10.11999/JEIT150125.

陈一畅, 张群姗, 陈校平, 等. 多重测量矢量模型下的稀疏步进频率SAR成像算法[J]. 电子与信息学报, 2014, 36(12): 2986-2993. doi: 10.3724/SP.J.1146.2013.01831.

CHEN Yichang, ZHANG Qunshan, CHEN Xiaoping, et al. An imaging algorithm of sparse stepped frequency SAR based on multiple measurement vectors model[J]. Journal of Electronics Information Technology, 2014, 36(12): 2986-2993. doi: 10.3724/SP.J.1146.2013.01831.

吴敏, 张磊, 邢孟道, 等. 基于分布式压缩感知的全极化雷达超分辨成像[J]. 电波科学学报, 2015, 30(1): 29-36. doi: 10.13443/j.cjors.2014041101.

WU Min, ZHANG Lei, XING Mengdao, et al. Full polarization super-resolution radar imaging algorithm based on distributed compressive sensing[J]. Chinese Journal of Radio Science, 2015, 30(1): 29-36. doi: 10.13443/j.cjors. 2014041101.

DUARTE M F, SARVOTHAM S, BARON D, et al. Distributed compressed sensing of jointly sparse signals[C]. Signals, Systems Computers, Asilomar, CA, USA, 2005: 1537-1541. doi: 10.1109/ACSSC.2005.1600024.

丁鹭飞, 耿富录, 陈建春. 雷达原理(第5版)[M]. 北京: 电子工业出版社, 2014: 1-2.

DING Lufei, GENG Fulu, and CHEN Jianchun. Radar Principles(5th)[M]. Beijing: Publishing House of Electronics Industry, 2014: 1-2.

SUNDMAN D. Greedy algorithms for distributed compressed sensing[D]. [Ph.D. dissertation], KTH Royal Institute of Technology, 2014: 99-116.

DUARTE M F and ELDAR Y C. Structured compressed sensing: from theory to applications[J]. IEEE Transactions on Signal Processing, 2011, 59(9): 4053-4083. doi: 10.1109/ TSP.2011.2161982.

DAVIES M E and ELDAR Y C. Rank awareness in joint sparse recovery[J]. IEEE Transactions on Information Theory, 2010, 58(2): 1135-1146. doi: 10.1109/TIT.2011.2173722.

张磊. 高分辨SAR/ISAR成像及误差补偿技术研究[D]. [博士论文], 西安电子科技大学, 2012.

ZHANG Lei. Study on high resolution SAR/ISAR imaging and error correction[D]. [Ph.D. dissertation], Xidian University, 2012.

HUANG Yajing, WANG Xuezhi, LI Xiangli, et al. Inverse synthetic aperture radar imaging using frame theory[J]. IEEE Transactions on Signal Processing, 2012, 60(10): 5191-5200. doi: 10.1109/TSP.2012.2208107.

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