A High-precision Method of the Rotation Compensation and Cross-range Scaling for ISAR Imaging

Xinge LIU; Mengdao XING; Guangcai SUN

doi:10.11999/JEIT171209

Volume 40 Issue 9

Aug. 2018

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(9): 2250-2257

Xinge LIU, Mengdao XING, Guangcai SUN. A High-precision Method of the Rotation Compensation and Cross-range Scaling for ISAR Imaging[J]. Journal of Electronics & Information Technology, 2018, 40(9): 2250-2257. doi: 10.11999/JEIT171209

Citation:

Xinge LIU, Mengdao XING, Guangcai SUN. A High-precision Method of the Rotation Compensation and Cross-range Scaling for ISAR Imaging[J]. Journal of Electronics & Information Technology, 2018, 40(9): 2250-2257. doi: 10.11999/JEIT171209

Citation:

PDF( 2054 KB)

A High-precision Method of the Rotation Compensation and Cross-range Scaling for ISAR Imaging

doi: 10.11999/JEIT171209

1.
National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
2.
Collaborative Innovation Center of Information Sensing and Understand, Xidian University, Xi’an 710071, China

Funds: The Foundation for Innovative Research Groups of the National Natural Science Foundation of China (61621005)

Received Date: 2017-12-21
Rev Recd Date: 2018-05-02

Available Online: 2018-07-12

Publish Date: 2018-09-01

Abstract

Abstract

Traditional Inverse SAR (ISAR) imaging algorithms neglect the impact of high-order rotational phase in the signal, which may make ISAR images of a target defocused. Further, the size of a target can not be obtained from ISAR image directly. In this study, an effective method to achieve the rotation compensation and cross-range scaling for ISAR imaging is proposed. Firstly, all the signals of the target are used to form the Local Average Doppler Trend (LADT) signal. Subsequently, RANdom SAmple Consensus (RANSAC) algorithm is performed to estimate the Doppler rate and effective rotational velocity. Finally, high-precision rotation compensation and cross-range scaling can be accomplished. Simulation and real data experiments validate the effectiveness of the proposed method.
- Inverse SAR (ISAR),
- Rotation compensation,
- Cross-range scaling,
- Doppler rate,
- RANdom SAmple Consensus (RANSAC)

FullText(HTML)

References(21)

References

XU Gang, YANG Lei, BI Guoan, et al. Enhanced ISAR imaging and motion estimation with parametric and dynamic sparse bayesian learning[J]. IEEE Transactions on Computational Imaging, 2017, 3(4): 940–952 doi: 10.1109/TCI.2017.2750330

XU Gang, XING Mengdao, ZHANG Lei, et al. Bayesian inverse synthetic aperture radar imaging[J]. IEEE Geoscience and Remote Sensing Letters, 2011, 8(6): 1150–1154 doi: 10.1109/LGRS.2011.2158797

吴敏, 张磊, 刘松杨, 等. OFDM-ISAR的稀疏优化成像与运动补偿[J]. 雷达学报, 2016, 5(1): 72–81 doi: 10.12000/JR16017

WU Min, ZHANG Lei, LIU Songyang, et al. OFDM-ISAR sparse optimization imaging and motion compensation[J]. Journal of Radars, 2016, 5(1): 72–81 doi: 10.12000/JR16017

谷文堃, 王党卫, 马晓岩. 分布式MIMO-ISAR子图像融合方法[J]. 雷达学报, 2017, 6(1): 90–97 doi: 10.12000/JR16042

GU Wenkun, WANG Dangwei, and MA Xiaoyan. Distributed MIMO-ISAR sub-image fusion method[J]. Journal of Radars, 2017, 6(1): 90–97 doi: 10.12000/JR16042

周叶剑, 张磊, 王虹现, 等. 空间轨道目标的逆合成孔径雷达成像质量分析[J]. 雷达学报, 2017, 6(1): 17–24 doi: 10.12000/JR16136

ZHOU Yejian, ZHANG Lei, WANG Hongxian, et al. Performance analysis on ISAR imaging of space targets[J]. Journal of Radars, 2017, 6(1): 17–24 doi: 10.12000/JR16136

邢孟道, 高悦欣, 陈溅来, 等. 海上舰船目标雷达成像算法[J]. 科技导报, 2017, 35(20): 53–60 doi: 10.3981/j.issn.1000-7857.2017.20.005

XING Mengdao, GAO Yuexin, CHEN Jianlai, et al. A survey of the radar imaging algorithms for ship targets on the sea[J]. Science and Technology Review, 2017, 35(20): 53–60 doi: 10.3981/j.issn.1000-7857.2017.20.005

高悦欣, 李震宇, 邢孟道, 等. 一种海面舰船目标ISAR成像时间段选择方法[J]. 西安电子科技大学学报, 2017, 44(2): 27–32 doi: 10.3969/j.issn.1001-400.017.0.005

GAO Yuexin, LI Zhenyu, XING Mengdao, et al. New ISAR imaging interval selection method for ship targets on the sea[J]. Journal of Xidian University, 2017, 44(2): 27–32 doi: 10.3969/j.issn.1001-400.017.0.005

HUANG Darong, FENG Cunqian, TONG Ningning, et al. 2D spatial-variant phase errors compensation for ISAR imagery based on contrast maximisation[J]. Electronics Letters, 2016, 52(17): 1480–1481 doi: 10.1049/el.2016.1595

盛佳恋, 张磊, 邢孟道, 等. 联合运动补偿的逆合成孔径雷达成像方位定标方法[J]. 系统工程与电子技术, 2014, 36(5): 859–865 doi: 10.3969/j.issn.1001-506X.2014.05.08

SHENG Jialian, ZHANG Lei, XING Mengdao, et al. Joint method of motion compensation and cross-range scaling for inverse synthetic aperture radar imaging[J]. Systems Engineering and Electronics, 2014, 36(5): 859–865 doi: 10.3969/j.issn.1001-506X.2014.05.08

李玺, 顾红, 刘国岁. ISAR成像中转角估计的新方法[J]. 电子学报, 2000, 28(6): 44–47 doi: 10.3321/j.issn:0372-2112.2000.06.012

LI Xi, GU Hong, and LIU Guosui. A method for estimating the rotation angle of the ISAR image[J]. Acta Electronica Sinica, 2000, 28(6): 44–47 doi: 10.3321/j.issn:0372-2112.2000.06.012

MARCO M. Novel approach for ISAR image cross-range scaling[J]. IEEE Transactions on Aerospace and Electronic Systems, 2008, 44(1): 281–294 doi: 10.1109/TAES.2008.4517004

SHENG Jialian, XING Mengdao, ZHANG Lei, et al. ISAR cross-range scaling by using sharpness maximization[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(1): 165–169 doi: 10.1109/LGRS.2014.2330625

XU Gang, YANG Lei, BI Guoan, et al. Maneuvering target imaging and scaling by using sparse inverse synthetic aperture[J]. Signal Processing, 2017, 137(C): 149–159 doi: 10.1016/j.sigpro.2017.01.016

XU Gang, XING Mengdao, ZHANG Lei, et al. Sparse apertures ISAR imaging and scaling for maneuvering targets[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(7): 2942–2956 doi: 10.1109/JSTARS.2014.2315630

MARTIN F and ROBERT B. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the ACM, 1981, 24(6): 381–395 doi: 10.1145/358669.358692

IMRE E and HILTON A. Order statistics of RANSAC and their practical application[J]. International Journal of Computer Vision, 2015, 111(3): 276–297 doi: 10.1007/s11263-014-0745-1

邢孟道, 保铮, 李真芳, 等. 雷达成像算法进展[M]. 北京: 电子工业出版社, 2014: 143–178.

徐刚, 杨磊, 张磊, 等. 一种加权最小熵的ISAR自聚焦算法[J]. 电子与信息学报, 2011, 33(8): 1809–1815 doi: 10.3724/SP.J.1146.2010.01153

XU Gang, YANG Lei, ZHANG Lei, et al. Weighted minimum entropy autofocus algorithm for ISAR imaging[J]. Journal of Electronics&Information Technology, 2011, 33(8): 1809–1815 doi: 10.3724/SP.J.1146.2010.01153

符吉祥, 孙光才, 邢孟道. 一种大转角ISAR两维自聚焦平动补偿方法[J]. 电子与信息学报, 2017, 39(12): 2889–2898 doi: 10.11999/JEIT170303

FU Jixiang, SUN Guangcai, and XING Mengdao. A two dimensional autofocus translation compensation method for wide-angle ISAR imaging[J]. Journal of Electronics&Information Technology, 2017, 39(12): 2889–2898 doi: 10.11999/JEIT170303

HUANG Darong, ZHANG Lei, XING Mengdao, et al. Doppler ambiguity removal and ISAR imaging of group targets with sparse decomposition[J]. IET Radar, Sonar&Navigation, 2016, 10(9): 1711–1719 doi: 10.1049/iet-rsn.2016.0036

STEVEN K. Fundamentals of Statistical Signal Processing: Estimation Theory[M]. New Jersey: Prentice-Hall PTR, 1993: 419–479.

Relative Articles

Supplements(0)

Cited By

Proportional views