针对低干扰比下信号分类难题，提出动态融合网络提升卫星导航抗干扰能力
JSR-GFNet: Jamming-to-Signal Ratio-Aware Dynamic Gating for Interference Classification in future Cognitive Global Navigation Satellite Systems
- 融合复数IQ数据与频谱图，用动态门控机制自适应分配信息权重
- 在10–50 dB干扰比范围内分类准确率显著提升，尤其在低信干比下表现优异
- 模型学习到物理可解释策略，适合航空航天高安全导航系统应用
向认知全球导航卫星系统（GNSS）接收机的演进要求精确的干扰分类以触发自适应抑制策略。然而，传统依赖时频分析（TFA）和卷积神经网络（CNN）的方法在低干扰信号比（JSR）环境下因噪声遮蔽导致性能严重下降，且仅使用幅度谱会丢失相位信息，引发特征退化，使高阶正交幅度调制与带限高斯噪声等谱形相似信号难以区分。为此，本文提出一种基于JSR感知的多模态融合网络（JSR-GFNet），将相位敏感的复数同相/正交（IQ）样本与短时傅里叶变换（STFT）谱图结合。其核心为受物理启发的动态门控机制，利用统计信号描述符作为条件控制器，自主估计信号可靠性并动态调节复值残差网络（IQ流）与EfficientNet主干（STFT流）的贡献权重。为验证模型，构建了涵盖21类干扰的综合GNSS干扰数据集（CGI-21），包含来自空中平台的软件定义波形。大量实验表明，JSR-GFNet在10–50 dB JSR全范围均取得更高分类精度。可解释性分析证实，模型学习到物理直观策略：在噪声受限场景侧重谱能积分，在高信噪比场景转向相位精度以解决调制模糊问题。该框架为下一代航空航天导航安全提供了鲁棒解决方案。
原文摘要 · Abstract (English)
The transition toward cognitive global navigation satellite system (GNSS) receivers requires accurate interference classification to trigger adaptive mitigation strategies. However, conventional methods relying on Time-Frequency Analysis (TFA) and Convolutional Neural Networks (CNNs) face two fundamental limitations: severe performance degradation in low Jamming-to-Signal Ratio (JSR) regimes due to noise obscuration, and ``feature degeneracy'' caused by the loss of phase information in magnitude-only spectrograms. Consequently, spectrally similar signals -- such as high-order Quadrature Amplitude Modulation versus Band-Limited Gaussian Noise -- become indistinguishable. To overcome these challenges, this paper proposes the \textbf{JSR-Guided Fusion Network (JSR-GFNet)}. This multi-modal architecture combines phase-sensitive complex In-Phase/Quadrature (IQ) samples with Short-Time Fourier Transform (STFT) spectrograms. Central to this framework is a physics-inspired dynamic gating mechanism driven by statistical signal descriptors. Acting as a conditional controller, it autonomously estimates signal reliability to dynamically reweight the contributions of a Complex-Valued ResNet (IQ stream) and an EfficientNet backbone (STFT stream). To validate the model, we introduce the Comprehensive GNSS Interference (CGI-21) dataset, simulating 21 jamming categories including software-defined waveforms from aerial platforms. Extensive experiments demonstrate that JSR-GFNet achieves higher accuracy across the full 10--50 dB JSR spectrum. Notably, interpretability analysis confirms that the model learns a physically intuitive strategy: prioritizing spectral energy integration in noise-limited regimes while shifting focus to phase precision in high-SNR scenarios to resolve modulation ambiguities. This framework provides a robust solution for next-generation aerospace navigation security.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。