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

TL;DR

This work tackles GNSS jammer localization in dynamic environments by equipping a moving four-antenna SDR with an IMU to create a synthetic aperture. It introduces a multimodal fusion pipeline that combines IQ data, FFT spectrograms, AoA features, and IMU pose to predict relative displacement and direction to the jammer. Evaluated on a large Fraunhofer L.I.N.K. dataset, the proposed method outperforms the McAFF baseline, achieving azimuth ~3.87° and elevation ~2.18°, though cross-jammer generalization remains challenging. The study highlights the benefits of motion-enabled spatial diversity and multimodal fusion for robust, physics-informed jammer localization in multipath-rich industrial 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 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.

Figures (26)

• Figure 1: Overview of the proposed pipeline. Raw measurements are acquired using a dynamic Ettus USRP and subsequently converted into IQ samples. Vision encoders are trained on spectrograms derived via Fast Fourier Transform (FFT) and are integrated with time-series models and statistical feature representations. Furthermore, five measurement sets are processed to estimate the relative motion using data from an inertial sensor.
• Figure 2: The figure illustrates successive positions of an SDR system with four antennas moving over time $t_{n-2}, t_{n-1}, t_n$ while measuring angle-of-arrival ($\alpha$) signals from an interference source.
• Figure 3: The recording setup includes an Ettus USRP X440 and a PC mounted at the 3D positioning system.
• Figure 4: Placement of four sweep antennas mounted at the bottom of the positioning system with $9\,cm$ distance.
• Figure 5: Jamming device 1 (blue).