摘要
基于分布式压缩感知理论,提出了一种全极化逆合成孔径雷达超分辨成像算法,联合各极化通道进行超分辨处理.首先,建立全极化信号模型及超分辨字典,利用各极化通道信号的联合稀疏性将全极化超分辨成像建模为最小L2,1范数的优化问题,运用一种快速算法求解该优化问题.由于利用联合稀疏约束,多极化通道联合成像相比于单通道成像能够获得更好的超分辨性能和噪声抑制能力,最终有效提高图像极化融合的效果.同时,采用快速傅里叶变换操作提升了算法的运算效率.基于backhoe的仿真数据实验验证了该算法的优越性.
A novel super-resolution imaging algorithm for full polarized inverse synthetic aperture radar(ISAR)is addressed.Based on the distributed compressive sensing(DCS)theory ajoint processing of polarization and super-resolution is realized.The fully polarized signal model is established,based on which the super-resolution dictionary is formed.By exploiting the joint sparsity between polarimetric channel signals,the fully polarized super-resolution imaging problem can be mathematically converted into a L2,1norm optimization question.The optimization problem can be solved via fast optimization algorithm.Comparing with the single-polarization imaging,the jointly multi-polarization imaging performs better on super-resolution and noise suppression by utilizing joint sparsity.Besides,the efficiency of the proposed algorithm can be improved by fast Fourier transform(FFT).Simulated experiments of the backhoe data verify the effectiveness of the proposed algorithm.
出处
《电波科学学报》
EI
CSCD
北大核心
2015年第1期29-36,共8页
Chinese Journal of Radio Science
基金
国家自然科学基金优秀青年基金(No.61222108)
国家自然科学基金(No.61101245)
关键词
逆合成孔径雷达
超分辨成像
分布式压缩感知
极化
inverse synthetic aperture radar(ISAR)
super-resolution imaging
distriubted compressive sensing(DCS)
polarization
