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

GNSS Jammer Direction Finding in Dynamic Scenarios Using an Inertial-based Multi-Antenna System Lucas Heublein∗, Thorsten Nowak†, Tobias Feigl∗, Jaspar Pahl∗, Felix Ott∗ ∗Fraunhofer Institute for Integrated Circuits IIS, 90411 N¨urnberg, Germany †Diehl Defence GmbH & Co. KG, 90552 R¨othenbach an der Pegnitz, Germany {lucas.heublein, tobias.feigl, jaspar.pahl, felix.ott}@iis.fraunhofer.de thorsten.nowak@diehl-defence.com Abstract—Jamming devices disrupt signals from the global navigation satellite system (GNSS) and pose a significant threat by compromising the reliability of accurate positioning. Con- sequently, 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. Index Terms—Global Navigation Satellite System, Jammer Lo- calization, Angle of Arrival, Direction of Arrival, IQ Components, FFT, Machine Learning, Inertial Data1 I. INTRODUCTION The localization accuracy of GNSS receivers is significantly compromised by interference signals emitted from jamming devices [1]–[4]. This problem has intensified in recent years, primarily due to the widespread availability of inexpensive jammers. Consequently, it is imperative to either mitigate the impact of such interference or eliminate its source alto- gether. Effective countermeasure implementation necessitates the detection, classification [5], and precise localization of the interference source. Traditional jammer localization methodologies – collec- tively referred to as direction-finding techniques – include approaches such as Received Signal Strength (RSS), Angle of Arrival (AoA) [6], [7], Direction of Arrival (DoA) [8], Time 1IEEE DGON Inertial Sensors and Applications (ISA), October 21-22 2025, Braunschweig, Germany Difference of Arrival (TDoA), and Frequency Difference of Arrival (FDoA) [9]. In AoA and DoA-based methods, the incident direction of the interference signal at the receiver is estimated [10]. However, these techniques are notably vulnerable to multipath effects, where reflected signals from structures such as buildings create multiple propagation paths. Such reflections can distort angle estimations, resulting in localization errors [11]. Furthermore, achieving high angular precision typically demands extensive antenna arrays and advanced hardware architectures, which substantially increase system cost and complexity. Machine learning (ML) approaches have emerged as a promising solution to overcome the inherent shortcomings of conventional AoA and DoA techniques in GNSS interference localization. ML models are capable of learning complex spatial and statistical patterns from data, enabling them to effectively characterize and mitigate multipath propagation effects [11]. By compensating for signal distortions induced by reflections, these methods enhance the precision of angle estimation. Moreover, ML-based frameworks can identify ob- structed propagation environments and adapt their estimations to maintain robustness under non-line-of-sight (NLoS) con- ditions [12]. Recent research efforts increasingly emphasize hybrid architectures that integrate traditional AoA techniques with ML-based inference models [8], [13], [14] to leverage the complementary strengths of both domains. The objective of this work is to develop a framework for the localization of interference sources in challenging NLoS scenarios dominated by multipath effects. In this study, we consider a scenario involving a mobile antenna receiver and a stationary interference source (jammer). The primary objective is to estimate the direction from the moving antenna to the jammer with high accuracy. Unlike conventional static-array approaches, the mobility of the an- tenna introduces an additional temporal dimension that can be exploited to enhance localization performance. By leveraging the antenna’s trajectory over time, it is possible to construct a synthetic aperture, effectively increasing the spatial div

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