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基于稀疏孔径的联合稀疏约束干涉ISAR机动目标三维成像

张榆红 邢孟道 徐刚

张榆红, 邢孟道, 徐刚. 基于稀疏孔径的联合稀疏约束干涉ISAR机动目标三维成像[J]. 电子与信息学报, 2015, 37(9): 2151-2157. doi: 10.11999/JEIT150125
引用本文: 张榆红, 邢孟道, 徐刚. 基于稀疏孔径的联合稀疏约束干涉ISAR机动目标三维成像[J]. 电子与信息学报, 2015, 37(9): 2151-2157. doi: 10.11999/JEIT150125
Zhang Yu-hong, Xing Meng-dao, Xu Gang. Joint Sparsity Constraint Interferometric ISAR Imaging for 3-D Geometry of Maneuvering Targets with Sparse Apertures[J]. Journal of Electronics & Information Technology, 2015, 37(9): 2151-2157. doi: 10.11999/JEIT150125
Citation: Zhang Yu-hong, Xing Meng-dao, Xu Gang. Joint Sparsity Constraint Interferometric ISAR Imaging for 3-D Geometry of Maneuvering Targets with Sparse Apertures[J]. Journal of Electronics & Information Technology, 2015, 37(9): 2151-2157. doi: 10.11999/JEIT150125

基于稀疏孔径的联合稀疏约束干涉ISAR机动目标三维成像

doi: 10.11999/JEIT150125

Joint Sparsity Constraint Interferometric ISAR Imaging for 3-D Geometry of Maneuvering Targets with Sparse Apertures

  • 摘要: InISAR系统能够实现对目标的3维几何估计,更加有利于目标的分类和识别。同时多功能ISAR/InISAR系统针对的多是机动性很强的目标,在某些情况下对单个目标仅能获取稀疏孔径观测,尤其是在目标存在机动特性的情况下,更是增加了ISAR成像的难度,这些对传统的ISAR成像算法提出了挑战。为了解决上述这些问题,该文针对机动目标提出一种基于稀疏孔径的联合稀疏约束InISAR 3维成像方法。对匀加速转动的目标,回波的多普勒调制可以建模成线性调频的形式,并用chirp-傅里叶字典来表征其机动性。接着将联合的多通道InISAR 2维成像转化为联合稀疏约束的最优化求解问题,并用改进的OMP算法进行求解。然后利用各个通道估计的ISAR图像和调频参数实现对目标的3维几何重构。相比于单通道独立成像,联合多通道稀疏约束成像能获得更好的2维和3维成像结果。最后,进行实测数据实验以验证该文算法的有效性。
  • 保铮, 邢孟道, 王彤. 雷达成像技术[M]. 北京: 电子工业出版社, 2005: 229-276.
    李丽亚, 刘宏伟, 曹向海, 等. 基于InISAR像的目标识别方法[J]. 电子与信息学报, 2008, 30(9): 2089-2093.
    Li Li-ya, Liu Hong-wei, Cao Xiang-hai, et al.. Radar automatic target recognition based on InISAR images[J]. Journal of Electronics Information Technology, 2008, 30(9): 2089-2093.
    李飞, 纠博, 刘宏伟. 基于随机霍夫变换的干涉ISAR横向定标算法[J]. 电子与信息学报, 2013, 35(1): 49-55.
    Li Fei, Jiu Bo, and Liu Hong-wei. A novel method of cross-range scaling interferometric ISAR based on randomized hough transform[J]. Journal of Electronics Information Technology, 2013, 35(1): 49-55.
    Wang Gen-yuan, Xia Xiang-gen, and Chen Victor C. Three-dimensional ISAR imaging of maneuvering target using three receivers[J]. IEEE Transactions on Image Processing, 2001, 10(3): 436-447.
    Zhang Qun, Yeo T S, Du Gan, et al.. Estimation of three-dimensional motion parameters in interferometric ISAR imaging[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(2): 292-300.
    Xu Gang, Xing Meng-dao, Zhang Lei, et al.. Sparse-apertures ISAR imaging and scaling for maneuvering targets[J]. IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, 2014, 7(7): 2942-2956.
    Liu Ya-bo, Li N, and Wang R. Achieving high-quality three-dimensional InISAR imageries of maneuvering target via super-resolution ISAR imaging by exploiting sparseness[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(4): 828-832.
    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.
    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.
    Xu Gang, Xing Meng-dao, and Zhang Lei. Bayesian inverse synthetic aperture radar imaging[J]. IEEE Geoscience and Remote Sensing Letters, 2011, 8(6): 1150-1154.
    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 Min, 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.
    Xia Xiang-gen. Discrete chirp-Fourier transform and its application to chirp rate estimation[J]. IEEE Transactions on Signal Processing, 2000, 48(11): 3122-3133.
    Guo Xin, Sun H B, and Wang S L. Comments on discrete chirp-Fourier transform and its application to chirp rate estimation[J]. IEEE Transactions on Signal Processing, 2002, 50(12): 3115-3116.
    Ramakrishnan N, Ertin E, and Moses R L. Enhancement of coupled multi-channel images using sparsity constraints[J]. IEEE Transactions on Image Processing, 2010, 19(8): 2115-2126.
    Eldar Y and Rauhut H. Average case analysis of multichannel sparse recovery using convex relaxation[J]. IEEE Transactions on Information Theory, 2010, 56(1): 505-519.
    Baraniuk R G, Cevher V, Duarte M F, et al.. Model-based compressive sensing[J]. IEEE Transactions on Information Theory, 2010, 56(4): 1982-2001.
    陈倩倩, 徐刚, 李亚超, 等. 短孔径ISAR方位定标[J]. 电子与信息学报, 2013, 35(8): 1854-1861.
    Chen Qian-qian, Xu Gang, Li Ya-chao, et al.. Cross-range scaling for ISAR with short aperture data[J]. Journal of Electronics Information Technology, 2013, 35(8): 1854-1861.
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出版历程
  • 收稿日期:  2015-01-22
  • 修回日期:  2015-04-14
  • 刊出日期:  2015-09-19

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