CS与GPR联合反演目标成像  

Joint Inversion of Compressive Sensing and Ground Penetrating Radar for Target Imaging

作　　者：陈兴东[1] 刘高嵩[1] 雷文太[1] 龙军[1] 

机构地区：[1]中南大学信息科学与工程学院,湖南长沙410083

出　　处：《雷达科学与技术》2012年第3期276-280,285,共6页Radar Science and Technology

基　　金：国家自然科学基金(No.60873081;M0921005;U0835003);高等学校博士学科点专项科研基金(No.20090162110072)

摘　　要：压缩传感(CS)理论是在已知信号具有稀疏性或可压缩性的条件下对信号数据进行采集、编解码的新理论。压缩传感采用非自适应线性投影来保持信号的原始结构,能通过数值最优化问题准确重构原始信号。压缩传感以远低于奈奎斯特频率进行采样,在高分辨压缩成像系统、视频图像采集系统、雷达成像以及MRI医疗成像等领域有着广阔的应用前景。阐述了压缩传感理论框架以及信号稀疏表示、CS编解码模型,并进行了压缩传感与探地雷达联合反演目标成像。反演结果表明,随机孔径压缩传感成像算法比递归反向投影算法和最小二乘法所需数据量少,成像效果好,目标旁瓣小,对噪声的鲁棒性更好。Compressed sensing（CS） theory is a novel data collection and coding theory under the condition that signal is sparse or compressible. It employs nonadaptive linear projections to preserve the structure of the signal. Signal reconstruction is conducted by using an optimization process from these projections. Different from the traditional signal acquisition process, compressive sensing is a new theory that captures and represents compressible signals at a sampling rate significantly below the Nyquist rate. It has broad applications such as high resolution compressive imaging, image and video processing systems, radar imaging, MPR imaging, etc. In this paper, the CS framework, CS coding model are introduced, and joint inversion of CS and GPR for target imaging are studied. The computer simulation results indicate that the random aperture measurement algorithm allow much fewer data, much shorter measurement time. And due to utilization of the sparse structure of interested target space, the method shows much more robust and sparse image than recursive back projection （RBP） and least square method.

关 键 词：压缩传感 探地雷达 联合反演 目标成像 随机孔径成像算法 

分 类 号：TN957.52[电子电信—信号与信息处理]