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

arXiv: https://arxiv.org/abs/2512.05128 · HTML: https://mlsanity.com/paper/2512.05128/

## Key facts

- Cited by 1 papers 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 (most cited first, 15 of 15)

- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Deep Networks for Direction-of-Arrival Estimation in Low SNR](https://mlsanity.com/paper/2011.08848/) (2020)
- [Neural Inertial Localization](https://mlsanity.com/paper/2203.15851/) (2022)
- [Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning](https://mlsanity.com/paper/2405.11067/) (2024)
- [Few-Shot Learning with Uncertainty-based Quadruplet Selection for Interference Classification in GNSS Data](https://mlsanity.com/paper/2402.09466/) (2024)
- [Evaluating ML Robustness in GNSS Interference Classification, Characterization & Localization](https://mlsanity.com/paper/2409.15114/) (2024)
- [Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification](https://mlsanity.com/paper/2410.15681/) (2024)
- [Achieving Generalization in Orchestrating GNSS Interference Monitoring Stations Through Pseudo-Labeling](https://mlsanity.com/paper/2410.14686/) (2024)
- [Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies](https://mlsanity.com/paper/2503.23775/) (2025)
- [Multi-Channel Attentive Feature Fusion for Radio Frequency Fingerprinting](https://mlsanity.com/paper/2303.10691/) (2023)
- [Towards an AI-Driven Universal Anti-Jamming Solution with Convolutional Interference Cancellation Network](https://mlsanity.com/paper/2203.09717/) (2022)
- [Neural Network-Based DOA Estimation in the Presence of Non-Gaussian Interference](https://mlsanity.com/paper/2301.02856/) (2023)
- [Robust Indoor Localization with Ranging-IMU Fusion](https://mlsanity.com/paper/2309.08803/) (2023)
- [Angle of Arrival Estimation with Transformer: A Sparse and Gridless Method with Zero-Shot Capability](https://mlsanity.com/paper/2408.09362/) (2024)
- [Target Localization with Jammer Removal Using Frequency Diverse Array](https://mlsanity.com/paper/1912.12564/) (2019)

## Built on by (most cited first, 1 of 1)

- [Physics-Informed Domain-Invariant Feature Learning with Autoencoder-Driven Gaussian Clustering for Robust Non-line-of-Sight Scenarios](https://mlsanity.com/paper/2607.02537/) (2026)

## 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.