融合IQ、FFT与AoA特征，提升复杂环境下的导航干扰源定位精度。
Attention-Based Fusion of IQ and FFT Spectrograms with AoA Features for GNSS Jammer Localization
- 用注意力机制融合IQ信号、FFT谱图和22个AoA特征
- 在动态多径环境下实现更精准的干扰源方位与距离估计
- 构建了室内移动干扰源数据集，适合抗干扰系统研发者
干扰设备会破坏全球导航卫星系统（GNSS）信号，威胁定位可靠性。因此，检测并定位干扰信号对态势感知、减轻影响及实施反制措施至关重要。传统到达角（AoA）方法在多径环境下因信号反射和散射导致定位误差增大，且阵列信号处理计算开销大。本文提出一种新方法，可同时检测、分类干扰，并估计干扰源的距离、方位角与仰角。通过基准测试筛选128种视觉编码器与时间序列模型，确定各任务最优方案。设计了一种基于注意力的融合框架，整合正交（IQ）采样数据与快速傅里叶变换（FFT）生成的谱图，同时引入22个AoA特征以提升定位精度。此外，构建了一个在室内动态多径环境中记录的移动干扰源数据集，并在性能上优于现有先进方法。
原文摘要 · Abstract (English)
Jamming devices disrupt signals from the global navigation satellite system (GNSS) and pose a significant threat by compromising the reliability of accurate positioning. Consequently, the detection and localization of these interference signals are essential to achieve situational awareness, mitigating their impact, and implementing effective counter-measures. Classical Angle of Arrival (AoA) methods exhibit reduced accuracy in multipath environments due to signal reflections and scattering, leading to localization errors. Additionally, AoA-based techniques demand substantial computational resources for array signal processing. In this paper, we propose a novel approach for detecting and classifying interference while estimating the distance, azimuth, and elevation of jamming sources. Our benchmark study evaluates 128 vision encoder and time-series models to identify the highest-performing methods for each task. We introduce an attention-based fusion framework that integrates in-phase and quadrature (IQ) samples with Fast Fourier Transform (FFT)-computed spectrograms while incorporating 22 AoA features to enhance localization accuracy. Furthermore, we present a novel dataset of moving jamming devices recorded in an indoor environment with dynamic multipath conditions and demonstrate superior performance compared to state-of-the-art methods.
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