mlsanityData to 14 Oct 2026

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

arXiv 2507.14167 ↗First posted 9 Jul 2025eess.SP cs.IR cs.LGcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 19 of its 27 references that are in the map. First posted on arXiv 9 Jul 2025.

Builds on

19 of 27 references.

References · 19

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
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  4. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,476
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  6. 2019InceptionTime: Finding AlexNet for Time Series Classificationcited by 387
  7. 2019CenterMask : Real-Time Anchor-Free Instance Segmentationcited by 201
  8. 2020Deep Networks for Direction-of-Arrival Estimation in Low SNRcited by 20
  9. 2024Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learningcited by 13
  10. 2024Few-Shot Learning with Uncertainty-based Quadruplet Selection for Interference Classification in GNSS Datacited by 12
  11. 2024Evaluating ML Robustness in GNSS Interference Classification, Characterization & Localizationcited by 11
  12. 2024Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classificationcited by 7
7 more
  1. 2024Achieving Generalization in Orchestrating GNSS Interference Monitoring Stations Through Pseudo-Labelingcited by 6
  2. 2025Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepanciescited by 5
  3. 2023Multi-Channel Attentive Feature Fusion for Radio Frequency Fingerprintingcited by 4
  4. 2025Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterizationcited by 4
  5. 2022Towards an AI-Driven Universal Anti-Jamming Solution with Convolutional Interference Cancellation Networkcited by 3
  6. 2023Neural Network-Based DOA Estimation in the Presence of Non-Gaussian Interferencecited by 3
  7. 2024Angle of Arrival Estimation with Transformer: A Sparse and Gridless Method with Zero-Shot Capabilitycited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Active Sensing with Meta-Reinforcement Learning for Emitter Localization from RF Observationscited by 0
  2. 2026Physics-Informed Domain-Invariant Feature Learning with Autoencoder-Driven Gaussian Clustering for Robust Non-line-of-Sight Scenarioscited by 0
  3. 2026The S-ICDF Dataset: Sionna-Simulated Dynamic Interference Characterization and Direction Findingcited by 0