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

2026-04-11

AI Summary

GNSS jammers severely degrade positioning reliability in multipath environments, while conventional angle-of-arrival (AoA) methods suffer from reflection/scattering artifacts and prohibitive computational overhead, limiting high-accuracy 3D localization. To address this, we propose an attention-based multimodal fusion framework that jointly processes IQ time-series, FFT spectrograms, and a 22-dimensional AoA feature vector for end-to-end estimation of jammer distance, azimuth, and elevation. To enable robust training and evaluation, we introduce the first indoor dynamic multipath dataset featuring mobile jammers. Through systematic benchmarking across 128 state-of-the-art vision and time-series models, our method achieves significant improvements in both 3D localization accuracy and jammer-type classification, outperforming all existing approaches. It effectively breaks the longstanding accuracy–efficiency trade-off under severe multipath conditions.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Localize GNSS jammers accurately in multipath environments
• Reduce computational cost of AoA-based jamming detection
• Enhance localization using IQ-FFT spectrograms with AoA features

Innovation

• Attention-based fusion of IQ and FFT spectrograms
• Integration of 22 AoA features for accuracy
• Novel dataset for dynamic multipath conditions