HRRPGraphNet：将HRRP转化为图结构以实现高效的目标准确识别
HRRPGraphNet: Make HRRPs to Be Graphs for Efficient Target Recognition
AI总结：
本文提出HRRPGraphNet，通过将HRRP数据转换为图结构，利用范围单元幅度向量和相对邻接矩阵，结合一维卷积和图卷积层提升目标识别的准确性和鲁棒性。
摘要：
高分辨率距离谱（HRRP）已成为雷达自动目标识别（RATR）领域的重要研究方向。尽管基于深度学习的HRRP识别方法取得了成功，但这些方法需要大量训练样本才能获得良好的性能，这在非合作环境下可能成为严重挑战。目前，基于深度学习的模型将HRRP视为序列，这可能导致忽略范围单元的内部关系。本文介绍HRRPGraphNet，其关键创新是将HRRP数据转换为新的图结构，利用基于范围单元幅度的节点向量和基于范围相对的邻接矩阵。该图方法通过一维卷积层进行局部特征提取，通过图卷积层和注意力模块进行全局特征提取。在飞机电磁模拟数据集上的实验表明，HRRPGraphNet在有限训练样本环境下表现出更高的准确性和鲁棒性，凸显了图驱动创新在HRRP基于RATR中的潜力。
英文摘要：
High Resolution Range Profiles (HRRP) have become a key area of focus in the domain of Radar Automatic Target Recognition (RATR). Despite the success of deep learning based HRRP recognition, these methods needs a large amount of training samples to generate good performance, which could be a severe challenge under non-cooperative circumstances. Currently, deep learning based models treat HRRP as sequences, which may lead to ignorance of the internal relationship of range cells. This letter introduces HRRPGraphNet, whose pivotal innovation is the transformation of HRRP data into a novel graph structure, utilizing a range cell amplitude(hyphen)based node vector and a range(hyphen)relative adjacency matrix. This graph(hyphen)based approach facilitates both local feature extraction via one(hyphen)dimensional convolution layers, global feature extraction through a graph convolution layer and a attention module. Experiments on the aircraft electromagnetic simulation dataset confirmed HRRPGraphNet superior accuracy and robustness, particularly in limited training sample environments, underscoring the potential of graph(hyphen)driven innovations in HRRP(hyphen)based RATR.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
