基于改进多重测量向量模型的SAR成像算法

陈一畅; 张群; 杨婷; 罗迎

doi:10.11999/JEIT151391

基于改进多重测量向量模型的SAR成像算法

doi: 10.11999/JEIT151391

1.
(空军工程大学信息与导航学院西安 710077) ②(复旦大学电磁波信息科学教育部重点实验室上海 200433) ③(空军预警学院黄陂士官学校武汉 430000) ④(中国人民解放军95133部队武汉 430000)

基金项目:

国家自然科学基金(61471386)，中国博士后基金(2015M570815)，陕西省统筹创新工程-特色产业创新链项目(2015KTTSGY04-06)

计量
- 文章访问数: 1386
- HTML全文浏览量: 124
- PDF下载量: 531
- 被引次数: 0
出版历程
- 收稿日期: 2015-12-09
- 修回日期: 2016-05-03
- 刊出日期: 2016-10-19

A Novel SAR Imaging Algorithm Based on Modified Multiple Measurement Vectors Model

1.
(Institute of Information and Navigation, Air Force Engineering University, Xi&rsquo

Funds:

The National Natural Science Foundation of China (61471386), The Postdoctoral Science Foundation of China (2015M570815), The Overall Innovation and Characteristic Industry Innovation Chain Project of Shaanxi Province (2015KTTSGY04-06)

摘要

摘要: 近年来，基于压缩感知(Compressed Sensing, CS)理论的稀疏场景SAR成像成为研究热点。在CS理论中，对于具有相同稀疏结构的联合稀疏目标信号源，多重测量向量(Multiple Measurement Vectors, MMV)模型可以获得优于单重测量矢量(Single Measurement Vector, SMV)模型的重构性能。然而，在距离徙动(Range Migration)不可忽略的应用场景，SAR各脉冲回波1维距离像具有不完全相同的稀疏结构，这使得无法在SAR应用中直接引入MMV模型。该文针对MMV模型与SAR距离徙动的矛盾，提出一种改进的MMV模型。在该模型下，各方位向位置对应的1维距离像的稀疏结构不要求完全相同，而是符合距离徙动特性。论文所提出的RM-OMP算法根据符合距离徙动特性的稀疏结构搜索稀疏信号支撑集，可以准确地重建稀疏信号源。论文采用仿真数据和实测数据实验验证了所提模型和算法的有效性。
- 合成孔径雷达 /
- 稀疏恢复 /
- 多重测量向量模型 /
- 距离徙动
Abstract: Recently, the Compressed Sensing (CS) theory becomes the researching hot point in SAR imaging. The Multiple Measurement Vectors (MMV) model of CS theory can be used to effectively represent the jointly sparse signals, and it can obtain better performance than Single Measurement Vector (SMV) model. Because the SAR range profiles at different pulses have different sparse structures, which result in the MMV model can not be directly used in the scenario of synthetic aperture radar imaging. In this paper, a modified MMV model is proposed for SAR imaging, and the Range Migration (RM) effect is embedded into the proposed model. Correspondingly, a modified Orthogonal Matching Pursuit (OMP) algorithm is developed to obtain the high-resolution range profile. Experiments based on simulated and measured data demonstrate the validity of the proposed model and the algorithm.
- Synthetic Aperture Radar (SAR) /
- Sparse reconstruction /
- Multiple Measurement Vectors (MMV) model /
- Range Migration (RM)

HTML全文

参考文献(20)

JUN Shi, ZHANG Xiaoling, and YANG Jianyu. Principle and methods on bistatic SAR signal processing via time correlation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(10): 3163-3178. doi: 10.1109/TGRS. 2008.920369.

SHEN Fangfang, ZHAO Guanghui, LIU Zicheng, et al. SAR imaging with structural sparse representation[J]. IEEE Journal of Selected Topocs in Applied Earth Observations and Remote Sensing, 2015, 8(8): 3902-3910. doi: 10.1109/JSTARS.2014.2364294.

BAMLER R. A comparison of Range-Doppler and wavenumber domain SAR focusing algorithm[J]. IEEE Transactions on Geoscience and Remote Sensing, 1992, 30(4): 706-713. doi: 10.1109/36.158864.

DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306. doi: 10.1109/TIT.2006.871582.

邱伟, 赵宏钟, 陈建军, 等. 基于平滑范数的高分辨雷达一维成像研究[J]. 电子与信息学报, 2011，33(12): 2869-2874. doi: 10.3724/SP.J.1146.2011.00418.

QIU Wei, ZHAO Hongzhong, CHEN Jianjun, et al. Highresolution radar one-dimendional imaging based on smoothed norm[J]. Journal of Electronics Information Technology, 2011, 33(12): 2869-2874. doi: 10.3724/SP. J.1146.2011.00418.

BU Hongxia, TAO Ran, BAI Xia, et al. A novel SAR imaging algorithm based on compressed sensing[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(5): 1003-1007. doi: 10.1106/LGRS.2014.2372319.

TELLO A M, LOPEZ D P, and MALLORQUI J J. A novel strategy for radar imaging based on compressive sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(12): 4285-4295. doi: 10.1109/TGRS.2010.2051231.

ONHON N O and CETIN M. A sparsity driven approach for joint SAR imaging and Phase error correction[J]. IEEE Transactions on Image Processing, 2011, 21(4): 2075-2088. doi: 10.1109/TIP.2011.2179056.

XIA Linuo, ZHU Daiyin, and JIANG Rui. A research on SAR autofocusing algorithms for compressive sensing technique [C]. IET International Radar Conference, Xi'an, 2013: 1-6.

GU Fu-fei, ZHANG Qun, CHI Long, et al. A novel motion compensating method for MIMO-SAR imaging based on compressed sensing[J]. IEEE Sensors Journal, 2015, 15(4): 2157-2165. doi: 10.1109/JSEN.2014.2371451.

CETIN M, STOJANOVIC I, ONHON N O, et al. Sparsity-driven synthetic aperture radar imaging: reconstruction, autofocusing, moving targets, and compressed sensing[J]. IEEE Signal Processing Magazine, 2014, 31(4): 27-40. doi: 10.1109/MSP.2014.2312834.

YANG Jiefang and ZHANG Yunhua. Novel compressive sensing-based Dechirp-Keystone algorithm for synthetic aperture radar imaging of moving target[J]. IET Radar, Sonar Navigation, 2015, 9(5): 509-518. doi: 10.1049/iet-rsn. 2014.0306.

CHEN J and HUO X. Theoretical results on sparse representations of multiple-measurement vectors[J]. IEEE Transactions on Signal Processing, 2006, 54(12): 4634-4643. doi: 10.1109/TSP.2006.881263.

YANG J, BOUZERDOUM A, TIVIVE F H C, et al. Multiple-measurement vector model and its application to through-the-wall radar imaging[C]. IEEE ICASS, Prague, Czech Republic, 2011: 2672-2675.

陈一畅, 张群, 陈校平, 等. 多重测量矢量模型下的稀疏步进频率SAR成像算法[J]. 电子与信息学报, 2014，36(12): 2986-2993. doi: 10.3724/SP.J.1146.2013.01831.

CHEN Yichang, ZHANG Qun, CHEN Xiaoping, et al. An imaging algorithm of sparse stepped frequency SAR based on multiple measurement vectors model[J]. Journal of Electronics Information Technology, 2014, 36(12): 2986-2993. doi: 10.3724/SP.J.1146.2013.01831.

TROPP J A, GILBERT A C, and STRAUSS M J. Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit[J]. Signal Processing, 2006, 86(3): 572-588. doi: 10.1016/j.sigpro.2005.05.030

TROPP J A and GILBERT A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2007, 53(12): 4655-4666. doi: 10.1109/TIT.2007.909108.

CUMMING L G and WONG F H. Digital Processing of Synthetic Aperture Radar Data: Algorithm and Implementation[M]. MA: Artech House, 2005: 393-394.

施引文献

资源附件(0)

访问统计