发表机构
University of Electronic Science and Technology of China (UESTC); Shenzhen Institute for Advanced Study, UESTC; Universitat Autònoma de Barcelona（电子科技大学; 电子科技大学深圳高等研究院; 巴塞罗那自治大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SPOC-Net通过将GNSS干扰识别分解为基本基元组合，利用多分辨率特征和结构化解码器，在实测数据上实现80.69%精确集准确率，并显著优于对比方法。
AI 中文摘要
可靠的定位、导航和授时支撑着智能交通、自主系统以及空天地一体化网络。然而，将每个混合信号视为单独类别的全球导航卫星系统（GNSS）干扰识别器难以扩展到新的组合。为此，本文提出SPOC-Net，将识别问题分解为识别一组基本干扰基元。多分辨率时频特征和学习到的基元查询为每种基元类型提供证据。高分辨率分支估计活跃类型的数量，结构化解码器将该估计与基元证据相结合以选择有效集合。训练时，实测的单基元记录是梯度优化中仅有的物理样本。训练期间按需组合其相关的干净同相和正交（IQ）序列，以生成具有不同相对功率和干扰噪声比的标记混合信号。来自十个训练列出的组合的独立实测混合信号支持模型选择和解码器校准；另外六个组合保留用于最终测试。对14,220个独立生成、传导组合并记录的射频混合信号进行评估，取得了80.69%的精确集准确率和92.84%的微平均F1分数。在模型开发中排除的组合上，SPOC-Net实现了80.89%的精确集准确率，在报告协议下超过最强对比方法18.77个百分点。
英文摘要
Reliable positioning, navigation, and timing support intelligent transportation, autonomous systems, and space-air-ground integrated networks. However, global navigation satellite system (GNSS) jamming recognizers that treat each mixture as a separate class are difficult to extend to new combinations. Therefore, this paper proposes SPOC-Net, which decomposes the recognition problem into identifying a set of basic jamming components. Multi-resolution time-frequency features and learned component queries provide evidence for each component type. A high-resolution branch estimates the number of active types, and a structured decoder combines this estimate with component evidence to select a valid set. For training, measured single-component records are the only physical samples used in gradient optimization. Their associated clean in-phase and quadrature (IQ) sequences are combined on demand during training to produce labeled mixtures with different relative powers and jamming-to-noise ratios. Separate measured mixtures from ten training-listed compositions support model selection and decoder calibration; six other compositions are reserved for final testing. Evaluation on 14,220 independently generated, conductively combined, and recorded radio frequency mixtures yields 80.69% exact-set accuracy and a 92.84% micro-averaged F1 score. On combinations excluded from model development, SPOC-Net achieves 80.89% exact-set accuracy, exceeding the strongest comparison method by 18.77 percentage points under the reported protocols.
