This letter develops a synthetic aperture radar (SAR) target classification method based on bidimensional variational mode decomposition (BVMD) and multitask compressive sensing (MTCS). BVMD is employed to decompose SAR images to exploit the time-frequency properties of the described targets. The MTCS is used to jointly classify the original SAR image and its BVMD components. So, the merits of BVMD and MTCS can be combined in the proposed method. Finally, based on the reconstruction errors, the target label can be decided. The Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset is used to set up experimental conditions to test the proposed method. By comparison with several reference methods from published works, the effectiveness and robustness of the proposed method can be confirmed.

这封信开发了一种基于二维变分模式分解（BVMD）和多任务压缩感测（MTCS）的合成孔径雷达（SAR）目标分类方法。BVMD用于分解SAR图像，以利用所描述目标的时频特性。MTCS用于对原始SAR图像及其BVMD分量进行联合分类。因此，可以将BVMD和MTCS的优点结合起来。最后，基于重构误差，可以确定目标标签。移动和静止目标获取与识别（MSTAR）数据集用于设置实验条件以测试所提出的方法。通过与已发表的著作中的几种参考方法进行比较，可以确认该方法的有效性和鲁棒性。