mlsanityData to 14 Oct 2026

GNSS Jammer Direction Finding in Dynamic Scenarios Using an Inertial-based Multi-Antenna System

arXiv 2512.05128 ↗First posted 23 Nov 2025eess.SP cs.AIcited by 1

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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 countermeasures. In this paper, we utilize a two-times-two patch antenna system (i.e., the software defined radio device Ettus USRP X440) to predict the angle, elevation, and distance to the jamming source based on in-phase and quadrature (IQ) samples. We propose to use an inertial measurement unit (IMU) attached to the antenna system to predict the relative movement of the antenna in dynamic scenarios. We present a synthetic aperture system that enables coherent spatial imaging using platform motion to synthesize larger virtual apertures, offering superior angular resolution without mechanically rotating antennas. While classical angle-of-arrival (AoA) methods exhibit reduced accuracy in multipath environments due to signal reflections and scattering, leading to localization errors, we utilize a methodology that fuses IQ and Fast Fourier Transform (FFT)-computed spectrograms with 22 AoA features and the predicted relative movement to enhance GNSS jammer direction finding.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 15 of its 32 references that are in the map. First posted on arXiv 23 Nov 2025.

Builds on

15 of 32 references.

References · 15

  1. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,546
  2. 2020Deep Networks for Direction-of-Arrival Estimation in Low SNRcited by 20
  3. 2022Neural Inertial Localizationcited by 13
  4. 2024Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learningcited by 13
  5. 2024Few-Shot Learning with Uncertainty-based Quadruplet Selection for Interference Classification in GNSS Datacited by 12
  6. 2024Evaluating ML Robustness in GNSS Interference Classification, Characterization & Localizationcited by 11
  7. 2024Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classificationcited by 7
  8. 2024Achieving Generalization in Orchestrating GNSS Interference Monitoring Stations Through Pseudo-Labelingcited by 6
  9. 2025Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepanciescited by 5
  10. 2023Multi-Channel Attentive Feature Fusion for Radio Frequency Fingerprintingcited by 4
  11. 2022Towards an AI-Driven Universal Anti-Jamming Solution with Convolutional Interference Cancellation Networkcited by 3
  12. 2023Neural Network-Based DOA Estimation in the Presence of Non-Gaussian Interferencecited by 3
3 more
  1. 2023Robust Indoor Localization with Ranging-IMU Fusioncited by 3
  2. 2024Angle of Arrival Estimation with Transformer: A Sparse and Gridless Method with Zero-Shot Capabilitycited by 2
  3. 2019Target Localization with Jammer Removal Using Frequency Diverse Arraycited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Physics-Informed Domain-Invariant Feature Learning with Autoencoder-Driven Gaussian Clustering for Robust Non-line-of-Sight Scenarioscited by 0