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全极化合成孔径雷达(SAR)可对不同极化通道分别独立进行压缩感知(CS)稀疏重建来增强成像性能,但分别独立处理没有利用极化信息的冗余性与互补性,有可能破坏极化信息的完整性。依据雷达目标在全极化下的散射特性构建联合稀疏度量函数,将全极化SAR高分辨成像转化为多通道联合稀疏约束的最优化重建问题,并用改进的正交匹配追踪算法进行求解。由于有效利用全极化信息,多通道联合CS成像相比于单通道CS成像能够获得更好的成像质量,还能全面准确反映目标全极化散射特性。通过对Backhoe挖掘机电磁仿真数据的处理,验证了算法的有效性,并且在微波暗室搭建了全极化SAR半实物仿真系统,利用其获取的全极化实测数据进一步验证了该方法的工程可行性。
Fully-polarized synthetic aperture radar (SAR) can independently perform compressive-sensing (CS) sparse reconstruction on different polarization channels to enhance the imaging performance. However, independently processing the redundancy and complementarity without utilizing polarization information, it is possible to destroy Polarization information integrity. According to the scattering characteristics of radar target under full polarization, a joint sparse metric function is constructed to transform the fully polarimetric SAR high resolution imaging into the optimal reconstruction of multi-channel joint sparse constraints. The improved orthogonal matching pursuit algorithm is used to solve the problem. Due to the efficient use of fully polarized information, multi-channel joint CS imaging can achieve better image quality than single-channel CS imaging and fully and accurately reflect the target all-polarization scattering characteristics. Through the processing of Backhoe excavator electromagnetic simulation data, the effectiveness of the algorithm is verified, and a fully-polarized SAR semi-physical simulation system is set up in a microwave anechoic chamber. The obtained fully-polarized measured data further verify the feasibility of the method Sex.