arXiv subjects

Alexander Rügamer

Publications and source records attributed to Alexander Rügamer.

9 recordsLinked to original sources

arXiv2026

Analyzing and Characterizing Multi-Source Interference Effects at Jammertest Norway 2025

Intentional radio-frequency interference from low-cost GNSS jammers increasingly threatens the accuracy and reliability of satellite-based positioning. Mitigating this threat requires not only detection but also robust waveform classification and characterization, direction-of-arrival inference for localization, and impact estimation on receiver performance under realistic operating conditions; all of which are challenged by strong distribution shifts across devices, sensors, environments, and satellite geometries. We address these challenges by compiling a dedicated real-world dataset recorded during Jammertest 2025 (Andoya, Norway), covering two outdoor test areas with parallel measurements from a single-antenna E1/E5 receiver module and a 2x2 CRPA-array. The dataset spans diverse jamming, spoofing, and meaconing scenarios, including CW, PRN, sweep/chirp, and multi-emitter configurations, and is complemented by per-recording metadata enabling time-aligned ground truth. Methodologically, we benchmark 17 machine learning (ML) architectures for interference-modulation recognition and multi-task characterization (type, occupied bandwidth, and signal strength), and we quantify navigation-relevant degradation via a receiver-aware spectral separation coefficient (SSC) that maps measured spectra to effective (C_s/N_0)_{eff} loss. To enable multi-source analysis, we transfer a YOLOv8s and RF-DETR detector pretrained on a labeled spectrogram dataset to localize multiple simultaneous interferers in GNSS spectrograms and subsequently characterize each detected component. Results show near-99% within-area classification accuracy but substantial cross-area performance drops, highlighting the need for robustness to real-world domain shifts.

eess.SP↗

arXiv2026

Receiver-Aware Analysis and Verification of the Spectral Separation Coefficient Under Interference-Induced Degradation

Interference poses a significant challenge to satellite-based positioning systems, making it essential to accurately quantify the effects of specific interference types on receiver performance and the resulting reliability of position computation. In current practice, interference effects are often quantified using receiver-independent metrics, with receiver-specific front-end characteristics either idealized or only implicitly considered. In this paper, we address this limitation by explicitly incorporating receiver-specific front-end characteristics into the computation of interference effects and validating the resulting receiver-dependent analysis experimentally. Therefore, we record a real-world open-field dataset comprising 210 distinct interference scenarios and compute the receiver-dependent spectral separation coefficient (SSC) and interference impact for a specific receiver module. Furthermore, we verify the computation using a controlled dataset generated with a radio frequency constellation simulator (RFCS), employing the same receiver module and replaying similar interferences classes. The comparison of results obtained in both environments demonstrates the robustness of the interference impact computation.

eess.SP↗

arXiv2026

GenAI for Energy-Efficient and Interference-Aware Compressed Sensing of GNSS Signals on a Google Edge TPU

Traditional methods for classifying global navigation satellite system (GNSS) jamming signals typically involve post-processing raw or spectral data streams, requiring complex and costly data transmission to cloud-based interference classification systems. In contrast, our proposed approach efficiently compresses GNSS data streams directly at the hardware receiver while simultaneously classifying jamming and spoofing attacks in real time. Given the growing prevalence of GNSS jamming, there is a critical need for real-time solutions suitable for power-constrained environments. This paper introduces a novel method for compressing and classifying GNSS jamming threats using generative artificial intelligence (GenAI), specifically variational autoencoders (VAEs), deployed on Google Edge tensor processing units (TPUs). The study evaluates various autoencoder (AE) architectures to compress and reconstruct GNSS signals, focusing on preserving interference characteristics while minimizing data size near the receiver hardware. The pipeline adapts large-scale AE models for Google Edge TPUs through 8-bit quantization to ensure energy-efficient deployment. Tests on raw in-phase and quadrature-phase (IQ) data, Fast Fourier Transform (FFT) data, and handcrafted features show the system achieves significant compression (>42x) and accurate classification of approximately 72 interference types on reconstructed signals (F2-score 0.915), closely matching the original signals (F2-score 0.923). The hardware-centric GenAI approach also substantially reduces jammer signal transmission costs, offering a practical solution for interference mitigation. Ablation studies on conditional and factorized VAEs (i.e., FactorVAE) explore latent feature disentanglement for data generation, enhancing model interpretability and fostering trust in machine learning (ML) solutions for sensitive interference applications.

cs.LG↗

arXiv2025

Evaluating ML Robustness in GNSS Interference Classification, Characterization & Localization

Jamming devices disrupt signals from the global navigation satellite system (GNSS) and pose a significant threat, as they compromise the robustness of accurate positioning. The detection of anomalies within frequency snapshots is crucial to counteract these interferences effectively. A critical preliminary countermeasure involves the reliable classification of interferences and the characterization and localization of jamming devices. This paper introduces an extensive dataset comprising snapshots obtained from a low-frequency antenna that capture various generated interferences within a large-scale environment, including controlled multipath effects. Our objective is to assess the resilience of machine learning (ML) models against environmental changes, such as multipath effects, variations in interference attributes, such as interference class, bandwidth, and signal power, the accuracy of jamming device localization, and the constraints imposed by snapshot input lengths. Furthermore, we evaluate the performance of a diverse set of 129 distinct vision encoder models across all tasks. By analyzing the aleatoric and epistemic uncertainties, we demonstrate the adaptability of our model in generalizing across diverse facets, thus establishing its suitability for real-world applications. Dataset: https://gitlab.cc-asp.fraunhofer.de/darcy_gnss/controlled_low_frequency

cs.AI↗

arXiv2025

VAE-based Feature Disentanglement for Data Augmentation and Compression in Generalized GNSS Interference Classification

Distributed learning and Edge AI necessitate efficient data processing, low-latency communication, decentralized model training, and stringent data privacy to facilitate real-time intelligence on edge devices while reducing dependency on centralized infrastructure and ensuring high model performance. In the context of global navigation satellite system (GNSS) applications, the primary objective is to accurately monitor and classify interferences that degrade system performance in distributed environments, thereby enhancing situational awareness. To achieve this, machine learning (ML) models can be deployed on low-resource devices, ensuring minimal communication latency and preserving data privacy. The key challenge is to compress ML models while maintaining high classification accuracy. In this paper, we propose variational autoencoders (VAEs) for disentanglement to extract essential latent features that enable accurate classification of interferences. We demonstrate that the disentanglement approach can be leveraged for both data compression and data augmentation by interpolating the lower-dimensional latent representations of signal power. To validate our approach, we evaluate three VAE variants - vanilla, factorized, and conditional generative - on four distinct datasets, including two collected in controlled indoor environments and two real-world highway datasets. Additionally, we conduct extensive hyperparameter searches to optimize performance. Our proposed VAE achieves a data compression rate ranging from 512 to 8,192 and achieves an accuracy up to 99.92%.

cs.LG↗

arXiv2025

Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning

The primary objective of methods in continual learning is to learn tasks in a sequential manner over time (sometimes from a stream of data), while mitigating the detrimental phenomenon of catastrophic forgetting. This paper proposes a method to learn an effective representation between previous and newly encountered class prototypes. We propose a prototypical network with a Bayesian learning-driven contrastive loss (BLCL), tailored specifically for class-incremental learning scenarios. We introduce a contrastive loss that incorporates novel classes into the latent representation by reducing intra-class and increasing inter-class distance. Our approach dynamically adapts the balance between the cross-entropy and contrastive loss functions with a Bayesian learning technique. Experimental results conducted on the CIFAR-10, CIFAR-100, and ImageNet100 datasets for image classification and images of a GNSS-based dataset for interference classification validate the efficacy of our method, showcasing its superiority over existing state-of-the-art approaches. Git: https://gitlab.cc-asp.fraunhofer.de/darcy_gnss/gnss_class_incremental_learning

cs.CV↗

arXiv2025

Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies

The accuracy and reliability of vehicle localization on roads are crucial for applications such as self-driving cars, toll systems, and digital tachographs. To achieve accurate positioning, vehicles typically use global navigation satellite system (GNSS) receivers to validate their absolute positions. However, GNSS-based positioning can be compromised by interference signals, necessitating the identification, classification, determination of purpose, and localization of such interference to mitigate or eliminate it. Recent approaches based on machine learning (ML) have shown superior performance in monitoring interference. However, their feasibility in real-world applications and environments has yet to be assessed. Effective implementation of ML techniques requires training datasets that incorporate realistic interference signals, including real-world noise and potential multipath effects that may occur between transmitter, receiver, and satellite in the operational area. Additionally, these datasets require reference labels. Creating such datasets is often challenging due to legal restrictions, as causing interference to GNSS sources is strictly prohibited. Consequently, the performance of ML-based methods in practical applications remains unclear. To address this gap, we describe a series of large-scale measurement campaigns conducted in real-world settings at two highway locations in Germany and the Seetal Alps in Austria, and in large-scale controlled indoor environments. We evaluate the latest supervised ML-based methods to report on their performance in real-world settings and present the applicability of pseudo-labeling for unsupervised learning. We demonstrate the challenges of combining datasets due to data discrepancies and evaluate outlier detection, domain adaptation, and data augmentation techniques to present the models' capabilities to adapt to changes in the datasets.

cs.CV↗

arXiv2024

Achieving Generalization in Orchestrating GNSS Interference Monitoring Stations Through Pseudo-Labeling

The accuracy of global navigation satellite system (GNSS) receivers is significantly compromised by interference from jamming devices. Consequently, the detection of these jammers are crucial to mitigating such interference signals. However, robust classification of interference using machine learning (ML) models is challenging due to the lack of labeled data in real-world environments. In this paper, we propose an ML approach that achieves high generalization in classifying interference through orchestrated monitoring stations deployed along highways. We present a semi-supervised approach coupled with an uncertainty-based voting mechanism by combining Monte Carlo and Deep Ensembles that effectively minimizes the requirement for labeled training samples to less than 5% of the dataset while improving adaptability across varying environments. Our method demonstrates strong performance when adapted from indoor environments to real-world scenarios.

eess.SP↗

arXiv2024

Few-Shot Learning with Uncertainty-based Quadruplet Selection for Interference Classification in GNSS Data

Jamming devices pose a significant threat by disrupting signals from the global navigation satellite system (GNSS), compromising the robustness of accurate positioning. Detecting anomalies in frequency snapshots is crucial to counteract these interferences effectively. The ability to adapt to diverse, unseen interference characteristics is essential for ensuring the reliability of GNSS in real-world applications. In this paper, we propose a few-shot learning (FSL) approach to adapt to new interference classes. Our method employs quadruplet selection for the model to learn representations using various positive and negative interference classes. Furthermore, our quadruplet variant selects pairs based on the aleatoric and epistemic uncertainty to differentiate between similar classes. We recorded a dataset at a motorway with eight interference classes on which our FSL method with quadruplet loss outperforms other FSL techniques in jammer classification accuracy with 97.66%. Dataset available at: https://gitlab.cc-asp.fraunhofer.de/darcy_gnss/FIOT_highway

eess.SP↗