中心博士生张林博的工作——Achieving High-Accuracy Target Recognition Using Few ISAR Images via Multi-Prototype Network with Attention Mechanism（基于多原型网络和注意力机制的小样本高精度ISAR目标识别）的相关成果近期被Electronics期刊接收发表，该工作得到了国家自然科学基金（T2225023, 62205203）的部分资助。由于ISAR图像特殊的成像机制，在雷达观测条件发生变化时，ISAR图像通常会出现未知的距离和方位角畸变，给目标探测带来障碍。并且，对于非合作探测目标，难以获得大量样本构建数据集。本文提出了一种基于注意力机制的多原型网络方案。多个原型的使用降低了单个原型的固定构造带来的不确定性，使网络能够获取更加丰富的目标信息。此外，注意力机制的引入能够生成更为鲁棒的多个原型，提高了多原型网络对ISAR图像的特征提取能力。实验结果表明，在四分类任务中，该方法的目标识别准确率达到95.08%，比其他几种小样本学习方法提高了9.94-17.49%。
摘要: Inverse synthetic aperture radar (ISAR) is a significant means of detection in space of non-cooperative targets, which means that the imaging geometry and associated parameters between the ISAR platform and the detection targets are unknown. In this way, a large number of ISAR images for high-accuracy target recognition are difficult to obtain. Recently, prototypical networks (PNs) have gained considerable attention as an effective method for few-shot learning. However, due to the specificity of the ISAR imaging mechanism, ISAR images often have unknown range and azimuth distortions, resulting in a poor imaging effect. Therefore, this condition poses a challenge for a PN to represent a class through a prototype. To address this issue, we use a multi-prototype network (MPN) with attention mechanism for ISAR image target recognition. The use of multiple prototypes eases the uncertainty associated with the fixed structure of a single prototype, enabling the capture of more comprehensive target information. Furthermore, to maximize the feature extraction capability of MPN for ISAR images, this method introduces the classical convolutional block attention module (CBAM) attentional mechanism, where CBAM generates attentional feature maps along channel and spatial dimensions to generate multiple robust prototypes. Experimental results demonstrate that this method outperforms state-of-the-art few-shot methods. In a four-class classification task, it achieved a target recognition accuracy of 95.08%, representing an improvement of 9.94–17.49% over several other few-shot approaches.