针对合成孔径雷达(SAR)目标识别问题, 设计并提出基于改进稀疏表示的方法。首先以传统稀疏表示分类(SRC)为基础, 在全局字典上求解稀疏表示系数矢量。在此基础上, 按照类别选择局部最佳字典, 并据此进行测试样本的重构表示, 最终, 通过比较不同类别的重构误差大小进行目标类别确认。实验中采用MSTAR数据集作为样本进行测试和验证。结果证明了所提方法的性能优势。

As for Synthetic Aperture Radar (SAR) target recognition,a method based on modified sparse representation is proposed.Firstly,based on traditional Sparse Representation-based Classification (SRC),the sparse representation coefficient vector is calculated on the global dictionary.Based on this,the optimal local dictionaries are selected for different training categories.Then,the test samples are reconstructed using different local dictionaries.Finally,the target categories of the test samples are determined by comparing the reconstruction errors of different categories.In the experiments,the MSTAR dataset is taken as samples for testing and validation.The superiority of the performance of the proposed method is confirmed.