检索规则说明:AND代表“并且”;OR代表“或者”;NOT代表“不包含”;(注意必须大写,运算符两边需空一格)
检 索 范 例 :范例一: (K=图书馆学 OR K=情报学) AND A=范并思 范例二:J=计算机应用与软件 AND (U=C++ OR U=Basic) NOT M=Visual
作 者:杨磊[1] 李埔丞 李慧娟 方澄 YANG Lei;LI Pucheng;LI Huijuan;FANG Cheng(Tianjin Key Laboratory for Advanced Signal Processing,Civil Aviation University of China,Tianjin 300300,China)
机构地区:[1]中国民航大学天津市智能信号与图像处理重点实验室
出 处:《电子与信息学报》2019年第12期2826-2835,共10页Journal of Electronics & Information Technology
基 金:国家自然科学基金(61601470);天津市自然科学基金(16JCYBJC41200);中央高校基本科研业务费专项资金(3122018C005)~~
摘 要:针对合成孔径雷达(SAR)成像中的稀疏特征增强问题,传统方法难以在精度与效率之间实现有效的平衡。该文提出基于复数交替方向多乘子方法(C-ADMM),针对SAR稀疏特征增强建立增广的拉格朗日优化方程,并引入复数范数邻近算子,基于高斯-赛德尔思想进行对偶迭代运算,从而在复数回波数据域内对多种SAR模式的实测数据进行成像。实验部分首先通过仿真数据的相变图(PTD)验证C-ADMM算法对于复数数据的稀疏恢复性能,然后选取地面静止场景和地面运动目标的原始SAR图像和逆SAR图像实测数据,与凸优化(CVX)方法和贝叶斯压缩感知(BCS)方法进行对比试验,最后验证了该文所提算法在稀疏特征增强应用中的稳健性、高效性和通用性。For the problem of sparse feature enhancement in Synthetic Aperture Radar(SAR)imagery,conventional methods are difficult to achieve a preferable balance between accuracy and efficiency.In this paper,a robust and efficient SAR imaging algorithm based on Complex Alternating Direction Method of Multipliers(C-ADMM)is proposed for general SAR imaging feature enhancement within complex raw data domain.The problem is firstly imposed by an augmented Lagrange function,and the complex ?1-norm of the intended SAR image is jointly formulated within the C-ADMM framework.Then,the proximal mapping of the sparse feature is derived as a soft-thresholding operator.Further,an iterative processing procedure is designed according to Gaussian-Deidel principle,and the convergence of the proposed algorithm is analyzed.In the experiment,the performance of the proposed algorithm is firstly examined by the simulated data in terms of Phase Transition Diagram(PTD)under different under-sampling rate and degree of sparsity.Then,various raw SAR and Inverse SAR(ISAR)data,for both stationary ground scene and Ground Moving Target Imaging(CMTIm),are applied to further verifying the proposed C-ADMM,and comparisons with classical Convex(CVX)and Bayesian Compress Sensing(BCS)algorithms are performed,so that both the effectiveness and superiority of the C-ADMM algorithm can be verified.
关 键 词:合成孔径雷达 稀疏特征增强 复数交替方向多乘子方法 增广拉格朗日优化方程
分 类 号:TN957.52[电子电信—信号与信息处理]
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在链接到云南高校图书馆文献保障联盟下载...
云南高校图书馆联盟文献共享服务平台 版权所有©
您的IP:216.73.216.28