JSR-GFNet: 针对未来认知全球导航卫星系统干扰分类的干扰-信号比感知动态门控
JSR-GFNet: Jamming-to-Signal Ratio-Aware Dynamic Gating for Interference Classification in future Cognitive Global Navigation Satellite Systems
AI总结：
JSR-GFNet通过结合相位敏感的IQ样本与STFT频谱图，利用动态门控机制提升GNSS干扰分类的准确性和鲁棒性。
摘要：
向认知全球导航卫星系统（GNSS）接收机的过渡需要准确的干扰分类以触发自适应抑制策略。然而，依赖时频分析（TFA）和卷积神经网络（CNNs）的传统方法面临两个根本性限制：在低干扰-信号比（JSR）情况下由于噪声遮蔽导致的严重性能下降，以及由于仅使用幅度谱图丢失相位信息导致的“特征退化”。因此，谱相似的信号——如高阶正交幅度调制与带限高斯噪声——变得无法区分。为克服这些挑战，本文提出了\textbf{JSR引导融合网络（JSR-GFNet）}。这种多模态架构结合了对相位敏感的复数同相/正交（IQ）样本与短时傅里叶变换（STFT）频谱图。该框架的核心是一个受物理启发的动态门控机制，由统计信号描述符驱动。作为条件控制器，它自主估计信号可靠性，以动态重新加权复数值残差网络（IQ流）和高效网络主干（STFT流）的贡献。为了验证模型，我们引入了综合GNSS干扰（CGI-21）数据集，模拟了21种干扰类别，包括来自空中平台的软件定义波形。大量实验表明，JSR-GFNet在完整的10-50 dB JSR频谱上实现了更高的准确性。值得注意的是，可解释性分析确认模型学习了一种物理上直观的策略：在噪声受限的场景中优先整合频谱能量，而在高SNR场景中则转向相位精度以解决调制歧义。该框架为下一代航空航天导航安全提供了稳健的解决方案。
英文摘要：
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.
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。
