Attention-Based Fusion of IQ and FFT Spectrograms with AoA Features for GNSS Jammer Localization

TL;DR

This paper introduces an attention-based fusion framework combining IQ samples, FFT spectrograms, and AoA features to improve GNSS jammer detection and localization accuracy in complex environments.

Contribution

It proposes a novel fusion approach integrating multiple data modalities and AoA features, along with a new dataset for moving jamming devices, advancing GNSS interference localization.

Findings

Superior localization accuracy over state-of-the-art methods

Effective detection and classification of jamming signals

Robust performance in dynamic multipath indoor environments

Abstract

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.