基于贝叶斯压缩感知的ISAR自聚焦成像

王天云; 陆新飞; 孙麟; 陈畅; 陈卫东

doi:10.11999/JEIT150235

基于贝叶斯压缩感知的ISAR自聚焦成像

doi: 10.11999/JEIT150235

2.
(中国科学技术大学电磁空间信息重点实验室合肥 230027) ②(中国卫星海上测控部江阴 214431)

基金项目:

国家自然科学基金(61172155, 61401140)和国家863计划项目(2013AA122903)

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出版历程
- 收稿日期: 2015-02-11
- 修回日期: 2015-06-29
- 刊出日期: 2015-11-19

An Autofocus Imaging Method for ISAR Based on Bayesian Compressive Sensing

2.
(Key Laboratory of Electromagnetic Space Information, University of Science and Technology of China, Hefei 230027, China)

Funds:

The National Natural Science Foundation of China (61172155, 61401140)

摘要

摘要: 针对ISAR自聚焦成像，该文提出一种基于贝叶斯压缩感知的高分辨率成像算法。首先利用目标图像的稀疏特性构建级联形式的稀疏先验模型，同时将相位误差建模为均匀分布模型；然后基于最大后验准则，依据贝叶斯压缩感知理论交替迭代求解目标图像和相位误差。与传统稀疏方法相比，所提算法进一步利用了目标图像的联合稀疏信息，将ISAR CS成像转化为MMV联合稀疏优化问题的求解，可以有效改善自聚焦的精度以及成像质量。仿真结果验证了该算法的有效性。
- 逆合成孔径雷达 /
- 自聚焦技术 /
- 高分辨成像 /
- 贝叶斯压缩感知
Abstract: For Inverse Synthetic Aperture Radar (ISAR) autofocus imaging, this paper proposes a high-resolution imaging method based on Bayesian Compressed Sensing (BCS). Firstly, according to the sparsity characteristics of target image, a sparse model with the hierarchical framework is established, which can achieve better approximation to the original l0 norm. Then, the phase errors are assumed to obey the uniform distribution. Next, following the criterion of Maximum A Posteriori (MAP), target image and phase errors are solved using alternate iteration based on BCS theory. Compared with traditional methods, the proposed method further combines the joint sparse information of target image, and converts the ISAR CS imaging into solving a joint Multiple Measurement Vector (MMV) sparse optimization problem, which can improve both the autofocus precision and the imaging quality efficiently. Simulation results show the effectiveness of the proposed method.
- Inverse Synthetic Aperture Radar (ISAR) /
- Autofocus technique /
- High-resolution imaging /
- Bayesian Compressive Sensing (BCS)

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参考文献(21)

Brisken S and Martella M. Multistatic ISAR autofocus with an image entropy-based technique[J]. IEEE Aerospace and Electronic Systems Magazine, 2014, 29(7): 30-36.

L Jie-qin, Huang Lei, Shi Yun-mei, et al.. Inverse synthetic aperture radar imaging via modified smoothed l0 norm[J]. IEEE Antennas and Wireless Propagation Letters, 2014, 13: 1235-1238.

吴敏, 邢孟道, 张磊. 基于压缩感知的二维联合超分辨 ISAR 成像算法[J]. 电子与信息学报, 2014, 36(1): 187-193.

Wu Ming, Xing Meng-dao, and Zhang Lei. Two dimensional joint super-resolution ISAR imaging algorithm based on compressive sensing[J]. Journal of Electronics Information Technology, 2014, 36(1): 187-193.

Odendaal J W, Barnard E, and Pistorius C W I. Two- dimensional superresolution radar imaging using the MUSIC algorithm[J]. IEEE Transactions on Antennas and Propagation, 1994, 42(10): 1386-1391.

Zhang Lei, Xing Meng-dao, Qiu Cheng-wei, et al.. Achieving higher resolution ISAR imaging with limited pulses via compressed sampling[J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(3): 567-571.

Rao Wei, Li Gang, Wang Xi-qin, et al.. Adaptive sparse recovery by parametric weighted l1 minimization for ISAR imaging of uniformly rotating targets[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(2): 942-952.

成萍, 司锡才, 姜义成, 等. 基于稀疏贝叶斯学习的稀疏信号表示 ISAR 成像方法[J]. 电子学报, 2008, 36(3): 547-550.

Cheng Ping, Si Xi-cai, Jiang Yi-cheng, et al.. Sparse signal representation ISAR imaging method based on sparse Bayesian learning[J]. Acta Electronica Sinica, 2008, 36(3): 547-550.

Liu Hong-chao, Jiu Bo, Liu Hong-wei, et al.. Superresolution ISAR imaging based on sparse Bayesian learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(8): 5005-5013.

Onhon N O and Cetin M. A sparsity-driven approach for joint SAR imaging and phase error correction[J]. IEEE Transactions on Image Processing, 2012, 21(4): 2075-2088.

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 Gang, Bao Min, Li Ya-chao, et al.. High precision ISAR imaging via Bayesian statistics[J]. Systems Engineering and Electronics, 2011, 33(11): 2382-2388.

Wahl D E, Eichel P H, Ghiglia D C, et al.. Phase gradient autofocus-a robust tool for high resolution SAR phase correction[J]. IEEE Transactions on Aerospace and Electronic Systems, 1994, 30(3): 827-835.

Li Xi, Liu Guo-sui, and Ni Jin-lin. Autofocusing of ISAR images based on entropy minimization[J]. IEEE Transactions on Aerospace and Electronic Systems, 1999, 35(4): 1240-1252.

Tzagkarakis G, Achim A, Tsakalides P, et al.. Joint reconstruction of compressively sensed ultrasound RF echoes by exploiting temporal correlations[C]. Proceedings of the IEEE 10th International Symposium on Biomedical Imaging, San Francisco, 2013: 632-635.

Tipping M E. Sparse Bayesian learning and the relevance vector machine[J]. The Journal of Machine Learning Research, 2001(1): 211-244.

Babacan S D, Molina R, and Katsaggelos A K. Bayesian compressive sensing using Laplace priors[J]. IEEE Transactions on Image Processing, 2010, 19(1): 53-63.

Du Xiao-yong, Duan Chong-wen, and Hu Wei-dong. Sparse representation based autofocusing technique for ISAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(3): 1826-1835.

Chen C C and Andrews H C. Target-motion-induced radar imaging[J]. IEEE Transactions on Aerospace and Electronic Systems, 1980, AES-16(1): 2-14.

Liu Hong-chao, Jiu Bo, Liu Hong-wei, et al.. Superresolution ISAR imaging based on sparse Bayesian learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(8): 5005-5013.

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