Due to Synthetic Aperture Radar (SAR) imaging characteristics, SAR vehicle recognition faces the problem of extracting discriminative and robust target features from a small dataset. Deep learning has shown impressive performance on the MSTAR dataset. However, data bias in a small dataset, such as background correlation, impairs the causality of these methods, i.e., discriminative features contain target and background differences. Moreover, different operating conditions of SAR lead to target signatures and background clutter variations in imaging results. However, many deep learning-based methods only verify robustness to target or background variations in the current experimental setting. In this paper, we propose a novel domain alignment framework named Hierarchical Disentanglement-Alignment Network (HDANet) to enhance features' causality and robustness. Concisely, HDANet consists of three parts: The first part uses data augmentation to generate signature variations for domain alignment. The second part disentangles the target features through a multitask-assisted mask to prevent non-causal clutter from interfering with subsequent alignment and recognition. Thirdly, a contrastive loss is employed for domain alignment to extract robust target features, and the SimSiam structure is applied to mitigate conflicts between contrastive loss and feature discrimination. Finally, the proposed method shows high robustness across MSTAR's multiple target, sensor, and environment variants. Noteworthy, we add a new scene variant to verify the robustness to target and background variations. Moreover, the saliency map and Shapley value qualitatively and quantitatively demonstrate causality. Our code is available in \url{https://github.com/waterdisappear/SAR-ATR-HDANet}.

翻译：由于合成孔径雷达（SAR）成像特性，SAR车辆识别面临从小数据集中提取判别性与鲁棒性目标特征的问题。深度学习方法在MSTAR数据集上展现出显著性能，但小数据集中的数据偏差（如背景相关性）削弱了这些方法的因果性——即判别性特征同时包含目标差异和背景差异。此外，不同SAR工作条件会导致成像结果中目标特征与背景杂波变化。然而，许多基于深度学习的方法仅验证了当前实验设置下对目标或背景变化的鲁棒性。本文提出一种新型域对齐框架——层次化解耦对齐网络（HDANet），以增强特征的因果性与鲁棒性。简而言之，HDANet由三部分组成：第一部分利用数据增强生成特征变化以进行域对齐；第二部分通过多任务辅助掩码解耦目标特征，防止非因果杂波干扰后续对齐与识别；第三部分采用对比损失进行域对齐以提取鲁棒目标特征，并应用SimSiam结构缓解对比损失与特征判别性之间的冲突。最终，所提方法在MSTAR的多目标、多传感器及多环境变体上展现出高鲁棒性。值得注意的是，我们新增了一种场景变体以验证对目标与背景变化的鲁棒性。此外，显著性图与Shapley值从定性和定量角度证明了因果性。代码见\url{https://github.com/waterdisappear/SAR-ATR-HDANet}。