基于深度网络的合成孔径雷达（synthetic aperture radar, SAR）目标识别方法，需要大量训练数据，而实际应用中SAR成像系统获取数量充足、分布均匀的目标数据难度极高。解决SAR目标识别小样本问题的途径之一，就是采用电磁仿真技术生成大量SAR仿真数据。然而仿真图像与实测SAR图像仍存在较大差异，直接使用仿真数据并不能带来目标识别性能的显著提升。针对上述问题，提出一种基于SAR目标特性约束的仿真数据优化方法。该方法在分析SAR目标特性的基础上，构建基于纹理结构-循环一致性的生成对抗网络（texture structure cycle-consistent generative adversarial network, TS-CycleGAN），采用结构相似度衡量指标约束CycleGAN的生成过程，降低仿真数据与实测数据之间的差异，提高仿真数据的可用性。针对SAR SAMPLE数据集的试验结果表明，所提出的方法相较于其他仿真数据优化方法，在图像质量评估和分类性能方面，都取得了明显的提升。

Synthetic aperture radar(SAR) target recognition method based on deep networks requires a large amount of training data, and in practical applications, it is extremely difficult for SAR imaging systems to obtain sufficient and evenly distributed target data. One way to solve the small sample problem in SAR target recognition, is to use electromagnetic simulation technology to generate a large amount of SAR simulation data. However, there are still significant differences between simulated images and measured SAR images, so using simulation data directly cannot bring significant performance improvement for target recognition. A simulation data optimization method based on SAR target characteristic constraints was proposed to address the above issues. On the basis of analyzing the characteristics of SAR targets, a texture structure cycle-consistent generative adversarial network (TS-CycleGAN) based on texture structure and cycle consistency was constructed, in which the structural similarity measure was used to constrain the generation process of CycleGAN. This method can reduce the difference between simulation data and measured data, and can improve the usability of simulation data. The experimental results on the SAR SAMPLE dataset show that, compared to other simulation data optimization methods, the proposed method achieves significant improvements in image quality evaluation and classification performance.