ISAR autofocusing imaging with sparse apertures and time-varying bistatic angle

ZHU Xiaoxiu; HU Wenhua; MA Juntao; GUO Baofeng; XUE Dongfang

doi:10.7527/S1000-6893.2018.22059

ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2018 , Vol. 39 >Issue 8: 322059 - 322059

DOI: https://doi.org/10.7527/S1000-6893.2018.22059

Electronics and Electrical Engineering and Control

ISAR autofocusing imaging with sparse apertures and time-varying bistatic angle

ZHU Xiaoxiu ,
HU Wenhua ,
MA Juntao ,
GUO Baofeng ,
XUE Dongfang

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Department of Electronic and Optical Engineering, Army Engineering University(Shijiazhuang Campus), Shijiazhuang 050003, China

Received date: 2018-01-29

Revised date: 2018-05-07

Online published: 2018-05-07

Supported by

Science Foundation of China (61601496)

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Abstract

To solve the problems of poor resolution of bistatic Inverse Synthetic Aperture Radar (ISAR) imaging with time-varying bistatic angle and image defocus caused by the space-varying phase error in traditional sparse aperture imaging, a high-resolution imaging algorithm integrated with phase error correction is proposed based on Bayesian Compressive Sensing (BCS). First, based on the echo data which have been compensated after motion compensation, a phase compensation term is constructed to compensate the Doppler shift caused by the time-varying bistatic angle. Second, a sparse basis matrix changing with the time-varying bistatic angle is constructed to establish a model for compressive sensing-based bistatic ISAR imaging with sparse apertures. The phase error is then treated as the modeling error in ISAR imaging. It is then assumed that each pixel of the target image follows a Laplace prior and noise follows Gaussian prior. Bayesian inference is used to realize non-ambiguous azimuth imaging integrated with phase error correction iteratively. The simulation results verify the validity and superiority of the proposed algorithm.

Key words： bistatic inverse synthetic aperture radar; time-varying bistatic angle; sparse aperture; autofocusing; Bayesian compressive sensing; Laplace prior

Cite this article

ZHU Xiaoxiu , HU Wenhua , MA Juntao , GUO Baofeng , XUE Dongfang . ISAR autofocusing imaging with sparse apertures and time-varying bistatic angle[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2018 , 39(8) : 322059 -322059 . DOI: 10.7527/S1000-6893.2018.22059

References

[1] 杨振起, 张永顺, 骆永军. 双(多)基地雷达系统[M]. 北京:国防工业出版社, 1998:14-15. YANG Z Q, ZHANG Y S, LUO Y J.Bistatic (multi-static) radar system[M]. Beijing:Defense Industry Press, 1998:14-15(in Chinese).
[2] MARTORELLA M, PALMER J, HOMER J, et al. On bistatic inverse synthetic aperture radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(3):1125-1134.
[3] 朱仁飞, 罗迎, 张群, 等. 双基地ISAR成像分析[J]. 现代雷达, 2011, 33(8):33-38. ZHU R F, LUO Y, ZHANG Q, et al. Analysis of bistatic inverse synthetic aperture radar imaging[J]. Modern Radar, 2011, 33(8):33-38(in Chinese).
[4] 郭宝锋, 尚朝轩, 王俊岭, 等. 双基地角时变下的逆合成孔径雷达越分辨单元徙动校正算法[J]. 物理学报, 2014, 63(23):238406. GUO B F, SHANG C X, WANG J L, et al. Correction of migration through resolution cell inbistatic inverse synthetic aperture radar in the presence of time-varying bistatic angle[J]. Acta Physica Sinica, 2014, 63(23):238406(in Chinese).
[5] LIN D, FAN L H, JIN L. Bistatic ISAR imaging algorithm based on compressed sensing[C]//The Proceedings of the Second International Conference on Communications, Signal Processing, and Systems.Berlin:Springer, 2014, 246:567-575.
[6] ZHANG S S, ZHANG W, ZONG Z L, et al, High-resolution bistatic ISAR imaging based on two-dimensional compressed sensing[J]. IEEE Transactions on Antennas and Propagation, 2015, 63(5):2098-2111.
[7] XU G, XING M D, CHEN Q Q, et al. High-resolution inverse synthetic aperture radar imaging and scaling with sparse aperture[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(8):4010-4027.
[8] 黄大荣, 郭兴荣, 张磊, 等. 稀疏孔径ISAR机动目标成像与相位补偿方法[J]. 航空学报, 2014, 35(7):2019-2030. HUANG D R, GUO X R, ZHANG L, et al. ISAR phase compensation and imaging of maneuvering target with sparse apertures[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(7):2019-2030(in Chinese).
[9] GUO B F, WANG J L, GAO M G, et al. Research on spatial-variant property of bistatic ISAR imaging plane of space target[J]. Chinese Physica B, 2015, 24(4):048402.
[10] BAE J H, KAE B S, LEE S H, et al. Bistatic ISAR image reconstruction using sparse-recovery interpolation of missing data[J]. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52(3):1155-1167.
[11] ZHANG L, WANG H X, QIAO Z J. Resolution enhancement for ISAR imaging via improved statistical compressive sensing[J]. EURASIP Journal on Advances in Signal Processing, 2016, 2016(1):1-19.
[12] DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52:1289-1360.
[13] 徐刚, 包敏, 李亚超, 等. 基于贝叶斯估计的高精度ISAR成像[J]. 系统工程与电子技术, 2011, 33(11):2382-2388. XU G, BAO M, LI Y C, et al. High precision ISAR imaging via Bayesian statistic[J]. Systems Engineering and Electronics, 2011, 33(11):2382-2388(in Chinese).
[14] ZHANG L, QIAO Z J, XING M D, et al. High-resolution ISAR imaging by exploiting sparse apertures[J]. IEEE Transactions on Antennas and Propagation, 2012, 60(2):997-1008.
[15] MA J T, GAO M G, GUO B F, et al. High resolution ISAR imaging of three-axis-stabilized space target by exploiting orbital and sparsepriors[J]. Chinese Physica B, 2017, 26(10):108401.
[16] JI S, XUE Y, CARIN L. Bayesian compressive sensing[J]. IEEE Transactions on Signal Process, 2008, 56(6):2346-2356.
[17] 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.
[18] 王天云, 陆新飞, 孙麟, 等. 基于贝叶斯压缩感知的ISAR自聚焦成像[J]. 电子与信息学报, 2015, 37(11):2719-2726. WANG T Y, LU X F, SUN L, et al. An autofocus imaging method for ISAR based on Bayesian compressive sensing[J]. Journal of Electronics and Information Technology, 2015, 37(11):2719-2726(in Chinese).
[19] BABACAN S D, MOLINA R, KATSAGGELOS A K. Bayesian compressive sensing using Laplace priors[J]. IEEE Transactions on Image Processing, 2010, 19(1):53-63.
[20] ANDREWS D F, MALLOWS C L. Scale mixtures of normal distributions[J]. Journal of the Royal Statistical Society, Series B, 1974, 36(1):99-102.
[21] TIPPING M E. Sparse Bayesian learning and the relevance vector machine[J]. Journal of Machine Learning Research, 2001, 1(3):211-244.

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