基于RID序列的微动目标高分辨三维成像方法

惠叶; 白雪茹

doi:10.12000/JR18056

基于RID序列的微动目标高分辨三维成像方法

DOI: 10.12000/JR18056

惠叶,
白雪茹^,

西安电子科技大学雷达信号处理国家重点实验室西安 710071

基金项目: 国家自然科学基金（61631019，61522114）

详细信息

作者简介:
惠　叶(1994–)，女，陕西西安人，2016年于西安电子科技大学获探测制导与控制技术专业工学学士学位，现攻读西安电子科技大学信号与信息处理专业博士学位。主要研究方向为雷达目标成像、雷达目标识别等。E-mail: xyyeah1994@126.com

白雪茹(1984–)，女，河北内邱人，2011年获西安电子科技大学工学博士学位，现为雷达信号处理国家级重点实验室教授、博导。主要研究方向为新体制雷达成像、基于高分辨图像的目标特征提取与识别等。E-mail: xrbai@xidian.edu.cn

通讯作者:
白雪茹 xrbai@xidian.edu.cn

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出版历程
- 收稿日期: 2018-07-23
- 修回日期: 2018-10-22
- 网络出版日期: 2018-10-28

RID Image Series-based High-resolution Three-dimensional Imaging of Micromotion Targets

Hui Ye,
Bai Xueru^,

National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

Funds: The National Natural Science Foundation of China (61631019, 61522114)

摘要

摘要: 微动是指目标或目标上某些部件沿雷达视线方向的小幅、非匀速运动。通过对微动目标进行逆合成孔径雷达(ISAR)高分辨3维成像，能够获得其结构和运动信息，从而为微动目标检测、跟踪、分类与识别提供重要依据，并在空间态势感知与防空反导中发挥着重要作用。由于微动目标运动形式复杂、回波非平稳性强，现有的参数化ISAR成像方法已经不再适用。针对该问题，该文提出基于散射中心航迹矩阵分解的微动目标高分辨3维成像方法。该方法首先生成距离-瞬时多普勒(RID)像序列，利用watershed图像分割方法提取RID像的散射中心支撑域，并基于最小欧氏距离准则实现航迹关联。然后，针对散射中心航迹关联时瞬时斜距估计精度受距离分辨率影响等问题，进一步提出基于现代谱估计的散射中心航迹矩阵精估计方法。最后，通过带约束的航迹矩阵分解实现微动目标的高分辨3维成像。仿真结果表明，该文所提的成像方法能够有效实现章动等复杂微动目标的高分辨3维成像。
- 逆合成孔径雷达(ISAR) /
- 微动 /
- 3维成像 /
- 距离-瞬时多普勒(RID)像序列 /
- 航迹关联
Abstract: Micromotion refers to the small and non-uniform motion of the target or several target components along the radar line of sight. Using the high-resolution three-Dimensional (3D) Inverse Synthetic Aperture Radar (ISAR) imaging, the structural information and motion status of micromotion targets can be obtained, providing essential features for the detection, tracking, identification, and classification, which play important roles in the space situation awareness and ballistic missile defense. Given the complex micromotion forms and the non-stationary radar echoes, the available parametric ISAR imaging methods are no longer applicable. To overcome this limitation, this study aims to propose a high-resolution 3D imaging method for micromotion targets based on the scattering center trajectory matrix decomposition. First, the Range Instantaneous Doppler (RID) image series is generated to extract the support region of scattering centers by the watershed method. Then, the scattering center association is achieved based on the minimum Euclidean distance criterion. Considering the insufficient accuracy in the instantaneous slant range estimation with limited range resolution, a method for refined estimation of the trajectory matrix based on the modern spectrum analysis is proposed. Finally, the high-resolution 3D imaging of the micromotion targets is obtained by the trajectory matrix decomposition with constraints. The simulation results have demonstrated that the proposed method could effectively obtain high-resolution 3D imaging of the targets in complex micromotions such as nutation.
- Inverse Synthetic Aperture Radar (ISAR) /
- Micro-motion /
- Three-Dimensional (3D) imaging /
- Range-Instantaneous Doppler (RID) image series /
- Trajectory association

HTML全文

图 1 RID像生成过程示意图

Figure 1. The process of RID image series generation

下载: 全尺寸图片幻灯片

图 2 基于航迹矩阵分解的微动目标高分辨成像算法流程图

Figure 2. The flow chart for high-resolution imaging of micro-motion targets based on trajectory matrix decomposition

下载: 全尺寸图片幻灯片

图 4 章动目标3维成像结果

Figure 4. 3D image of the nutation target

下载: 全尺寸图片幻灯片

下载: 全尺寸图片幻灯片

图 5 均方根误差随信噪比的变化曲线

Figure 5. Variation of the RMSE with SNR

下载: 全尺寸图片幻灯片

参考文献(34)

[1]	Chen V C, Li F Y, Ho S S, et al. Micro-Doppler effect in radar: Phenomenon, model, and simulation study[J]. IEEE Transactions on Aerospace and Electronic Systems, 2006, 42(1): 2–21. DOI: 10.1109/TAES.2006.1603402
[2]	Chen V C. The Micro-Doppler Effect in Radar[M]. Boston: Artech House, 2011.
[3]	Brown W M and Fredricks R J. Range-Doppler imaging with motion through resolution cells[J]. IEEE Transactions on Aerospace and Electronic Systems, 1969, AES-5(1): 98–102. DOI: 10.1109/TAES.1969.309826
[4]	Carrara W G, Goodman R S, and Majewski R M. Spotlight Synthetic Aperture Radar: Signal Processing Algorithms[M]. Boston: Artech House, 1995.
[5]	张翼, 朱玉鹏, 黎湘. 基于微多普勒特征的目标微动参数估计[J]. 信号处理, 2009, 25(7): 1120–1124. DOI: 10.3969/j.issn.1003-0530.2009.07.022 Zhang Yi, Zhu Yu-peng, and Li Xiang. Micro-motion parameter estimation of ballistic missile target based on micro-Doppler feature[J]. Signal Processing, 2009, 25(7): 1120–1124. DOI: 10.3969/j.issn.1003-0530.2009.07.022
[6]	Wang T, Wang X S, Chang Y L, et al. Estimation of precession parameters and generation of ISAR images of ballistic missile targets[J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(4): 1983–1995. DOI: 10.1109/TAES.2010.5595608
[7]	Luo Y, Zhang Q, Qiu C W, et al. Micro-Doppler effect analysis and feature extraction in ISAR imaging with stepped-frequency chirp signals[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(4): 2087–2098. DOI: 10.1109/TGRS.2009.2034367
[8]	邹小海, 艾小锋, 李永祯, 等. 基于微多普勒的圆锥弹头进动与结构参数估计[J]. 电子与信息学报, 2011, 33(10): 2413–2419. DOI: 10.3724/SP.J.1146.2011.00120 Zou Xiao-hai, Ai Xiao-feng, Li Yong-zhen, et al. Precession and structural parameter estimation of the cone-shaped warhead based on the micro-Doppler[J]. Journal of Electronics&Information Technology, 2011, 33(10): 2413–2419. DOI: 10.3724/SP.J.1146.2011.00120
[9]	Luo Y, Zhang Q, Yuan N, et al. Three-dimensional precession feature extraction of space targets[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(2): 1313–1329. DOI: 10.1109/TAES.2014.110545
[10]	Zhang Q, Yeo T S, Tan H S, et al. Imaging of a moving target with rotating parts based on the Hough transform[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(1): 291–299. DOI: 10.1109/TGRS.2007.907105
[11]	Gao H W, Xie L G, Wen S L, et al. Micro-Doppler signature extraction from ballistic target with micro-motions[J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(4): 1969–1982. DOI: 10.1109/TAES.2010.5595607
[12]	Bai X R and Bao Z. High-resolution 3D imaging of precession cone-shaped targets[J]. IEEE Transactions on Antennas and Propagation, 2014, 62(8): 4209–4219. DOI: 10.1109/TAP.2014.2329004
[13]	Bai X R and Bao Z. Imaging of rotation-symmetric space targets based on electromagnetic modeling[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(3): 1680–1689. DOI: 10.1109/TAES.2014.120772
[14]	Bai X R and Bao Z. High-resolution radar imaging of aerospace targets with micromotion[C]. Proceedings of 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada, 2014: 934–937. DOI: 10.1109/IGARSS.2014.6946579.
[15]	Chen V C and Ling H. Time-Frequency Transforms for Radar Imaging and Signal Analysis[M]. Boston, MA, USA: Artech House, 2002.
[16]	Tomasi C and Kanade T. Shape and motion from image streams under orthography: A factorization method[J]. International Journal of Computer Vision, 1992, 9(2): 137–154. DOI: 10.1007/BF00129684
[17]	Bai X R, Zhou F, and Bao Z. High-resolution radar imaging of space targets based on HRRP series[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(5): 2369–2381. DOI: 10.1109/TGRS.2013.2260342
[18]	Chen V C and Qian S. Joint time-frequency transform for radar range-Doppler imaging[J]. IEEE Transactions on Aerospace and Electronic Systems, 1998, 34(2): 486–499. DOI: 10.1109/7.670330
[19]	Chen V C. Adaptive time-frequency ISAR processing[C]. Proceedings Volume 2845, Radar Processing, Technology, and Applications, Denver, CO, United States, 1996. DOI: 10.1117/12.257216.
[20]	Chen V C. Radar detection of multiple moving targets in clutter using time-frequency radon transform[C]. Proceedings Volume 4728, Signal and Data Processing of Small Targets 2002, Orlando, FL, United States, 2002. DOI: 10.1117/12.478534.
[21]	Bai X R, Zhou F, and Bao Z. High-resolution three-dimensional imaging of space targets in micromotion[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(7): 3428–3440. DOI: 10.1109/JSTARS.2015.2431119
[22]	Wang Q, Xing M D, Lu G Y, et al. SRMF-CLEAN imaging algorithm for space debris[J]. IEEE Transactions on Antennas and Propagation, 2007, 55(12): 3524–3533. DOI: 10.1109/TAP.2007.910343
[23]	Wang Q, Xing M D, Lu G Y, et al. High-resolution three-dimensional radar imaging for rapidly spinning targets[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(1): 22–30. DOI: 10.1109/TGRS.2007.909086
[24]	Bai X R, Xing M D, Zhou F, et al. High-resolution three-dimensional imaging of spinning space debris[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(4): 2352–2362. DOI: 10.1109/TGRS.2008.2010854
[25]	Chen V C. Reconstruction of inverse synthetic aperture radar image using adaptive time-frequency wavelet transform[C]. Proceedings Volume 2491, Wavelet Applications II, Orlando, FL, United States, 1995, DOI: 10.1117/12.205404.
[26]	Ferrara M, Arnold G, and Stuff M. Shape and motion reconstruction from 3D-to-1D orthographically projected data via object-image relations[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(10): 1906–1912. DOI: 10.1109/TPAMI.2008.294
[27]	Mayhan J T, Burrows M L, Cuomo K M, et al. High resolution 3D " Snapshot” ISAR imaging and feature extraction[J]. IEEE Transactions on Aerospace and Electronic Systems, 2001, 37(2): 630–642. DOI: 10.1109/7.937474
[28]	Burrows M L. Two-dimensional ESPRIT with tracking for radar imaging and feature extraction[J]. IEEE Transactions on Antennas and Propagation, 2004, 52(2): 524–532. DOI: 10.1109/TAP.2003.822411
[29]	Grewal M S and Andrews A P. Kalman Filtering, Theory and Practice Using MATLAB[M]. New York, USA: Wiley, 2001.
[30]	Allen J. Short term spectral analysis, synthesis, and modification by discrete Fourier transform[J]. IEEE Transactions on Acoustics,Speech,and Signal Processing, 1977, 25(3): 235–238. DOI: 10.1109/TASSP.1977.1162950
[31]	Vincent L and Soille P. Watersheds in digital spaces: An efficient algorithm based on immersion simulations[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, 13(6): 583–598. DOI: 10.1109/34.87344
[32]	Stoica P and Moses R L. Spectral Analysis of Signals[M]. Upper Saddle River, NJ: Prentice Hall, 2005.
[33]	Liu H C, Jiu B, Liu H W, 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
[34]	Li H T, Wang C Y, Wang K, et al. High resolution range profile of compressive sensing radar with low computational complexity[J]. IET Radar,Sonar&Navigation, 2015, 9(8): 984–990. DOI: 10.1049/iet-rsn.2014.0454