多元经验模态分解及在SAR图像目标识别中的应用

提出基于多元模态分解的合成孔径雷达（SAR）目标识别方法。多元模态分解是传统模态分解的多元扩展，能够有效避免传统算法中的模态混叠问题。采用多元模态分解对SAR图像进行处理，获得的多层次固有模式函数（IMF）能够更为有效地反映目标的时频特性。不同IMF之间具有良好互补性，同时它们描述同一目标因而具有内在关联性。分类阶段，采用联合稀疏表示对分解得到的IMF进行表征。联合稀疏表示在多任务学习的理念下，对多个关联稀疏表示问题进行求解，可获得更为可靠的估计结果。在获得各层次IMF对应的稀疏表示系数矢量的基础上，计算不同类别对于当前测试样本多层次IMF的重构误差之和，进而判定测试样本的目标类别。基于MSTAR数据集开展实验，通过在标准操作条件、俯仰角差异、噪声干扰以及目标遮挡条件下进行对比分析，验证了提出方法的有效性。

Multivariate empirical mode decomposition with application to SAR image target recognition

A synthetic aperture radar (SAR) target recognition method was proposed based on multivariate empirical mode decomposition (MEMD). MEMD was the general extension of traditional EMD, which could avoid the mode mixing problems. MEMD was employed to process SAR images to obtain the multi-layer intrinsic mode functions (IMF), which could better reflect the time-frequency properties of the targets. Different layers of IMFs could effectively complement each other while sharing some inner correlations because they are generated from the same target. In the classification phase, the joint sparse representation was employed to represent the IMFs. The joint sparse representation could solve several related sparse representation tasks based on the idea of multi-task learning. It could produce more precise estimations than the solutions of single tasks. According to the sparse coefficient vectors corresponding to different IMFs, the reconstruction errors of different classes for the representation of the test sample can be calculated. Afterwards, the target label of the test sample can be determined. Experiments were conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, by comparison with existing methods under the standard operating condition, depression angle variance, noise corruption, and target occlusion, the results confirm the validity of the proposed method.