For the synthetic aperture radar (SAR) target recognition problem, a method combining multifeature joint classification and adaptive weighting is proposed with innovations in fusion strategies. Zernike moments, nonnegative matrix factorization (NMF), and monogenic signal are employed as the feature extraction algorithms to describe the characteristics of original SAR images with three corresponding feature vectors. Based on the joint sparse representation model, the three types of features are jointly represented. For the reconstruction error vectors from different features, an adaptive weighting algorithm is used for decision fusion. That is, the weights are adaptively obtained under the framework of linear fusion to achieve a good fusion result. Finally, the target label is determined according to the fused error vector. Experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset under the standard operating condition (SOC) and four extended operating conditions (EOC), i.e., configuration variants, depression angle variances, noise interference, and partial occlusion. The results verify the effectiveness and robustness of the proposed method.

中文翻译：

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多特征联合表示与自适应加权相结合在SAR目标识别中的应用

针对合成孔径雷达（SAR）目标识别问题，提出了一种融合多特征联合分类和自适应加权的融合方法。使用Zernike矩，非负矩阵分解（NMF）和单基因信号作为特征提取算法，以描述具有三个相应特征向量的原始SAR图像的特征。在联合稀疏表示模型的基础上，对三种类型的特征进行联合表示。对于来自不同特征的重构误差向量，将自适应加权算法用于决策融合。即，在线性融合的框架下自适应地获得权重，以获得良好的融合结果。最后，根据融合误差向量确定目标标签。在标准操作条件（SOC）和四个扩展操作条件（EOC）下对移动和静止目标获取与识别（MSTAR）数据集进行了实验，即配置变体，俯角变化，噪声干扰和部分遮挡。结果证明了该方法的有效性和鲁棒性。