基于稀疏孔径干涉ISAR技术的复杂运动舰船目标三维坐标恢复方法

王勇; 陈雪飞

doi:10.12000/JR18019

Three-dimensional Geometry Reconstruction of Ship Targets with Complex Motion for Interferometric ISAR with Sparse Aperture

DOI: 10.12000/JR18019

Wang Yong^,,
Chen Xuefei

Research Institute of Electronic Engineering Technology, Harbin Institute of Technology, Harbin 150001, China

Funds: The National Natural Science Foundation of China (61622107, 61471149), The Fundamental Research Funds for the Central Universities.

More Information

Author Bio:
Wang Yong (SM’16) was born in 1979. He received the B. S. degree and M. S. degree from Harbin Institute of Technology (HIT), Harbin, China, in 2002 and 2004, respectively, both in electronic engineering. He received the Ph. D. degree in information and communication engineering from HIT in 2008. He is currently a professor with the institute of electronic engineering technology in HIT. His main research interests are in the fields of time frequency analysis of nonstationary signal, radar signal processing, and their application in synthetic aperture radar (SAR) imaging. Dr. Yong Wang has published more than 60 papers, and most of them appeared in the journals of IEEE Trans. On GRS, IET Signal Processing, Signal Processing, etc. He received the Program for New Century Excellent Talents in University of Ministry of Education of China in 2012, and the Excellent Doctor’s Degree nomination Award in China in 2010. E-mail: wangyong6012@hit.edu.cn

Chen Xuefei received the B. S. degree from Harbin Institute of Technology (HIT), Harbin, China, in 2017. She is now pursuing the M. E. degree in Harbin Institute of Technology. Her current research interests include the field of InISAR imaging, time-frequency signal analysis and ISAR imaging of the target with sparse aperture

Corresponding author: Wang Yong wangyong6012@hit.edu.cn

摘要

摘要: 基于正交双基线的3维干涉逆合成孔径雷达(ISAR)成像技术可获得目标的3维坐标信息，这对目标的分类与识别是非常有利的。然而，实际情况下回波数据一般都是稀疏的，这对传统的干涉成像技术带来一定的挑战。该文提出一种稀疏孔径情况下的舰船目标3维干涉成像算法，并采用最小熵方法实现回波数据的运动补偿与图像配准，同时基于梯度算子实现对稀疏数据的精确恢复。通过对方位向数据进行参数估计与压缩处理，可获得目标的2维ISAR成像结果，进而基于干涉技术实现对复杂运动舰船目标的3维成像。仿真数据验证了文中方法的有效性。
- 3维干涉ISAR成像 /
- 稀疏孔径 /
- 梯度算法 /
- 参数估计
Abstract: Three-Dimensional (3-D) Interferometric Inverse Synthetic Aperture Radar (InISAR) imaging system based on the orthogonal double baseline can achieve the 3-D geometric reconstruction of a target effectively, which is extremely helpful in target classification and identification. However, only sparse aperture measurements are available in the actual imaging process, which might pose some challenges to the traditional InISAR imaging algorithms. In this study, a new method of 3-D InISAR imaging of a ship with sparse aperture is presented. Minimum entropy algorithms are adopted to realize motion compensation and image coregistration of the sparse echoes. A gradient-based technique is used to achieve highly accurate signal reconstruction for the sparse aperture. A two-Dimensional (2-D) ISAR image was achieved with azimuth compression via the parameters-estimation method, and the 3-D reconstruction of a ship was achieved via the interference approach. The obtained simulation results validate the feasibility of the presented approach.
- Three-Dimensional (3-D) Interferometric Inverse Synthetic Aperture Radar (InISAR) /
- Sparse aperture /
- Gradient-based method /
- Parameter estimation

HTML全文

Figure 1. 3-D InISAR imaging model

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Figure 2. The 3-D imaging model in XOY plane

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Figure 3. RMS

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Figure 4. GMS

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Figure 5. The ideal model for the ship

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Figure 6. RMS of the echoes from radar A with 2/4 sparse aperture

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Figure 7. GMS of the echoes from radar A with 2/4 sparse aperture

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Figure 9. 2-D ISAR images of radar A in 2/4 sparse aperture (GMS)

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Figure 10. 3-D reconstruction for the ship in 2/4 sparse aperture data (RMS)

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Figure 11. 3-D reconstruction for the ship in 1/4 sparse aperture data (RMS)

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Figure 12. 3-D reconstruction for the ship in 2/4 sparse aperture data (GMS)

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Figure 13. 3-D reconstruction for the ship in 1/4 sparse aperture data (GMS)

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Figure 15. The images of the target with 2/4 sparse aperture data (GMS) by using the OMP algorithm

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Figure 8. 2-D ISAR images of radar A in 2/4 sparse aperture (RMS)

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Figure 14. The images of the target with 2/4 sparse aperture data (RMS) by using the OMP algorithm

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Figure 16. Calculation and analysis of ${{\vec R}_{Ap}}\left( {{t_m}} \right)$

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Table 1. Simulation parameters for the ship with complicated movement

Parameter	Value
Carrier frequency	10 GHz
Pulse width	20 us
Imaging time	2 s
Band width	400 MHz
Amplitude of roll	2.3 ${{π}} $/180
Amplitude of pitch	2.5 ${{π}} $/180
Amplitude of yaw	4.8 ${{π}} $/180
Length of the baseline	2 m
Sampling frequency	25.6 MHz
Number of the pulse	512
Pulse repetition frequency	256 Hz
Angular velocity of roll	2 ${{π}} $/12.2
Angular velocity of pitch	2 ${{π}} $/6.7
Angular velocity of yaw	2 ${{π}} $/14.2

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