Sparse ISAR Imaging Algorithm Based on Bayesian-Lasso
-
摘要: 逆合成孔径雷达(ISAR)目标回波具有明显的稀疏特征，传统的凸优化稀疏ISAR成像算法涉及繁琐的正则项系数调整，严重限制了超分辨成像的精度及便捷程度。针对此问题，该文面向非约束Lasso正则化模型，建立分层贝叶斯概率模型，将非约束的
$ {\ell _1}$ 范数正则化问题等效转化成稀疏拉普拉斯先验建模问题，并在分层贝叶斯Lasso模型中建立正则项系数依赖的概率分布，从而为实现完全自动化参数调整提供便利条件。考虑到目标稀疏散射特征和多超参数的高维统计特性，该文应用吉布斯(Gibbs)随机采样方法，实现对ISAR目标稀疏特征的求解，并同步获取包括正则项系数在内的多参数估计。基于该文研究方法可实现全部参数均通过数据学习获得，从而有效避免繁琐的参数调整过程，提升算法的自动化程度。仿真及实测数据均可证明该方法的有效性和优越性。Abstract: Due to the echoes of the Inverse Synthetic Aperture Radar (ISAR) imagery are spatially sparse, the conventional convex optimization for the sparse image recovery involves tedious adjustment for the regularization parameter, which seriously limits the accuracy and the convenience of the image formation. In this paper, the unconstrained least absolute shrinkage and selection operator (Lasso) model is introduced for the$ {\ell _1}$ regularization problem, and it is equivalently transformed into sparse Bayesian inference under the Laplacian prior. More specifically, a hierarchical Bayesian model is established. In such cases, multiple hyper-parameters with multi-level conditional probability distribution are introduced. Due to the equivalent transformation, the manual choice of the regularization parameter can be replaced by automatic determination under the hierarchical Bayesian model, which provides convenience of fully conditional probability adjustment. Considering the high dimensions of sparse image responses and multiple hyper-parameters, the Gibbs sampler is adopted, where the Bayesian posterior of the ISAR image and high-dimensional hyper-parameters can be solved with fully confidence. Based on the research in this paper, all parameters can be attained by data, therefore tedious parameter adjustment can be avoided, and the automation level of the algorithm can be improved. The effectiveness and superiority of this method are proved by both simulated and measured data experiments. -
表 1 贝叶斯Lasso算法流程
(1) 初始化${{{X}}^{\left( 0 \right)}}$, ${\beta ^{\left( 0 \right)}}$, ${{{\alpha}} ^{\left( 0 \right)}}$和${\lambda ^{\left( 0 \right)}}$并构造字典${{A}}$； (2) 设置老化期门限$T$和迭代次数$k$=$1,2, ··· ,K$，其中$K$为迭代总次数且$T < K$，开始循环采样； (3) 根据式(15)噪声精度$\beta $的条件后验概率密度分布$p\left( {\beta \left| {{{X}},{{Y}},{{\alpha}} ,\lambda } \right.} \right)$对$\beta $进行采样； (4) 根据式(16)正则化系数$\lambda $所服从的条件后验概率密度分布$p\left( {\lambda \left| {{{X}},{{Y}},{{\alpha}} ,\beta } \right.} \right)$对$\lambda $进行采样； (5) 根据式(17)超参数${{\alpha}} $所服从的条件后验概率密度分布$p\left( {{{\alpha}} \left| {{{X}},{{Y}},\lambda ,\beta } \right.} \right)$对${{\alpha}} $进行采样； (6) 根据式(19)目标${{X}}$所服从的条件后验概率密度分布$p\left( {{{X}}\left| {{{Y}},{{\alpha}} ,\lambda ,\beta } \right.} \right)$对${{X}}$进行采样； (7) 若$k > T$，收集${{X}}$的采样样本$\left[ {{{{X}}^{\left( T \right)}},{{{X}}^{\left( {T + 1} \right)}}, ··· ,{{{X}}^{\left( K \right)}}} \right]$，重复步骤(3)—步骤(6)直至收敛。 
下载: 导出CSV
-
保铮, 邢孟道, 王彤. 雷达成像技术[M]. 北京: 电子工业出版社, 2005.BAO Zheng, XING Mengdao, and WANG Tong. Radar Imaging Technology[M]. Beijing: Publishing House of Electronics Industry, 2005. 杨利超, 邢孟道, 孙广才, 等. 一种微波光子雷达ISAR成像新方法[J]. 电子与信息学报, 2019, 41(6): 1271–1279. doi: 10.11999/JEIT180661YANG Lichao, XING Mengdao, SUN Guangcai, et al. A novel ISAR imaging algorithm for microwave photonics radar[J]. Journal of Electronics &Information Technology, 2019, 41(6): 1271–1279. doi: 10.11999/JEIT180661 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. doi: 10.11999/JEIT150235WANG Tianyun, LU Xinfei, SUN Lin, et al. An autofocus imaging method for ISAR based on Bayesian compressive sensing[J]. Journal of Electronics &Information Technology, 2015, 37(11): 2719–2726. doi: 10.11999/JEIT150235 DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289–1306. doi: 10.1109/TIT.2006.871582 王钢, 周若飞, 邹昳琨. 基于压缩感知理论的图像优化技术[J]. 电子与信息学报, 2020, 42(1): 222–233. doi: 10.11999/JEIT190669WANG Gang, ZHOU Ruofei, and ZOU Yikun. Research on image optimization technology based on compressed sensing[J]. Journal of Electronics &Information Technology, 2020, 42(1): 222–233. doi: 10.11999/JEIT190669 TELLO M, LOPEZ-DEKKER 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 YANG Lei, ZHAO Lifan, ZHOU Song, et al. Sparsity-driven SAR imaging for highly maneuvering ground target by the combination of time-frequency analysis and parametric bayesian learning[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(4): 1443–1454. doi: 10.1109/jstars.2016.2611005 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 吕杰勤. 基于压缩感知的ISAR成像算法研究[D]. [硕士论文], 哈尔滨工业大学, 2014.LÜ Jieqin. Study on algorithm of inverse synthetic radar imaging based on compressive sensing[D]. [Master dissertation], Harbin Institute of Technology, 2014. WONG W K and ZHOU Julie. CVX-based algorithms for constructing various optimal regression designs[J]. Canadian Journal of Statistics, 2019, 47(3): 374–391. doi: 10.1002/cjs.11499 WANG Xiangrong, ELIAS A, and AMIN M G. Thinned array beampattern synthesis by iterative soft-thresholding-based optimization algorithms[J]. IEEE Transactions on Antennas and Propagation, 2014, 62(12): 6102–6113. doi: 10.1109/TAP.2014.2364048 杨磊, 李埔丞, 李慧娟, 等. 稳健高效通用SAR图像稀疏特征增强算法[J]. 电子与信息学报, 2019, 41(12): 2826–2835. doi: 10.11999/JEIT190173YANG Lei, LI Pucheng, LI Huijuan, et al. Robust and efficient sparse-feature Enhancementfor generalized SAR imagery[J]. Journal of Electronics &Information Technology, 2019, 41(12): 2826–2835. doi: 10.11999/JEIT190173 李瑞, 张群, 苏令华, 等. 基于稀疏贝叶斯学习的双基雷达关联成像[J]. 电子与信息学报, 2019, 41(12): 2865–2872. doi: 10.11999/JEIT180933LI Rui, ZHANG Qun, SU Linghua, et al. Bistatic radar coincidence imaging based on sparse bayesian learning[J]. Journal of Electronics &Information Technology, 2019, 41(12): 2865–2872. doi: 10.11999/JEIT180933 ZHAO Lifan, WANG Lu, BI Guoan, et al. An autofocus technique for high-resolution inverse synthetic aperture radar imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(10): 6392–6403. doi: 10.1109/TGRS.2013.2296497 YANG Lei, ZHAO Lifan, BI Guoan, et al. SAR ground moving target imaging algorithm based on parametric and dynamic sparse bayesian learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(4): 2254–2267. doi: 10.1109/TGRS.2015.2498158 WANG Lu, ZHAO Lifan, BI Guoan, et al. Enhanced ISAR imaging by exploiting the continuity of the target scene[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(9): 5736–5750. doi: 10.1109/TGRS.2013.2292074 陈倩倩, 邢孟道, 李浩林, 等. 一种适用于低信噪比短CPI的ISAR横向定标算法[J]. 西安电子科技大学学报, 2014, 41(6): 12–17, 64. doi: 10.3969/j.issn.1001-2400.2014.0603CHEN Qianqian, XING Mengdao, LI Haolin, et al. Cross-range scaling for ISAR imaging within short CPI and low SNR[J]. Journal of Xidian University, 2014, 41(6): 12–17, 64. doi: 10.3969/j.issn.1001-2400.2014.0603 侯丽丽, 郑明洁, 宋红军, 等. 多通道高分辨率宽测绘带SAR系统杂波抑制技术研究[J]. 电子与信息学报, 2016, 38(3): 635–642. doi: 10.11999/JEIT150659HOU Lili, ZHENG Mingjie, SONG Hongjun, et al. Research on clutter suppression for multichannel High-resolution wide-swath SAR system[J]. Journal of Electronics &Information Technology, 2016, 38(3): 635–642. doi: 10.11999/JEIT150659 XU Gang, GAO Yandong, LI Jinwei, et al. InSAR phase denoising: A review of current technologies and future directions[J]. IEEE Geoscience and Remote Sensing Magazine, 2020, 8(2): 64–82. doi: 10.1109/MGRS.2019.2955120 张群英, 江兆凤, 李超, 等. 太赫兹合成孔径雷达成像运动补偿算法[J]. 电子与信息学报, 2017, 39(1): 129–137. doi: 10.11999/JEIT160201ZHANG Qunying, JIANG Zhaofeng, LI Chao, et al. Motion compensation imaging algorithm of TeraHertz synthetic aperture radar[J]. Journal of Electronics &Information Technology, 2017, 39(1): 129–137. doi: 10.11999/JEIT160201 YANG Lei, LI Pucheng, ZHANG Su, et al. Cooperative multitask learning for sparsity-driven SAR imagery and nonsystematic error autocalibration[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(7): 5132–5147. doi: 10.1109/TGRS.2020.2972972 YANG Lei, BI Guoan, XING Mengdao, et al. Airborne SAR moving target signatures and imagery based on LVD[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(11): 5958–5971. doi: 10.1109/TGRS.2015.2429678 DONOHO D L and TANNER J. Precise undersampling theorems[J]. Proceedings of the IEEE, 2010, 98(6): 913–924. doi: 10.1109/JPROC.2010.2045630 -
图(8) / 表(1)
计量
- 文章访问数: 2494
- HTML全文浏览量: 620
- PDF下载量: 112
- 被引次数: 0
下载:
