arXiv subjects

Jonathan Ott

Publications and source records attributed to Jonathan Ott.

6 recordsLinked to original sources

arXiv2026

The S-ICDF Dataset: Sionna-Simulated Dynamic Interference Characterization and Direction Finding

Jamming and spoofing threaten wireless and satellite navigation by disrupting or manipulating radio frequency (RF) signals, undermining availability, integrity, and trust. Robust interference monitoring (i.e., detection, classification, characterization, and direction finding) is therefore essential to identify and localize anomalous signals. While machine learning (ML) promises improved performance in complex environments, its development and validation depend on large-scale datasets that capture realistic signal and channel variability. Collecting such data in the real world is difficult because intentional jamming is illegal and ground-truth attribution is confounded by propagation, hardware, and environmental effects. To address this gap, we create and publish S-ICDF, a large-scale indoor interference dataset generated with Sionna, a GPU-accelerated simulation library for physical-layer wireless communications. S-ICDF covers 102 interference configurations, including diverse antenna array patterns, bandwidths, and simulation settings such as noise level and reflection depth. We further provide baseline results by benchmarking S-ICDF with classical estimation and direction finding (DF) methods (MUSIC, ESPRIT, and CAPON) and with modern ML approaches. The dataset is publicly available at: https://gitlab.cc-asp.fraunhofer.de/darcy_gnss/sicdf_dataset

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arXiv2026

Exploitation of Hidden Context in Dynamic Movement Forecasting: A Neural Network Journey from Recurrent to Graph Neural Networks and General Purpose Transformers

Forecasting within signal processing pipelines is crucial for mitigating delays, particularly in predicting the dynamic movements of objects such as NBA players. This task poses significant challenges due to the inherently interactive and unpredictable nature of sports, where abrupt changes in velocity and direction are prevalent. Traditional approaches, including (S)ARIMA(X), Kalman filters (KF), and Particle filters (PF), often struggle to model the non-linear dynamics present in such scenarios. Machine learning (ML) methods, such as long short-term memory (LSTM) networks, graph neural networks (GNNs), and Transformers, offer greater flexibility and accuracy but frequently fail to explicitly capture the interplay between temporal dependencies and contextual interactions, which are critical in chaotic sports environments. In this paper, we evaluate these models and assess their strengths and weaknesses. Experimental results reveal key performance trade-offs across input history length, generalizability, and the ability to incorporate contextual information. ML-based methods demonstrated substantial improvements over linear models across forecast horizons of up to 2s. Among the tested architectures, our hybrid LSTM augmented with contextual information achieved the lowest final displacement error (FDE) of 1.51m, outperforming temporal convolutional neural network (TCNN), graph attention network (GAT), and Transformers, while also requiring less data and training time compared to GAT and Transformers. Our findings indicate that no single architecture excels across all metrics, emphasizing the need for task-specific considerations in trajectory prediction for fast-paced, dynamic environments such as NBA gameplay.

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arXiv2026

Resilient Channel Charting Under Varying Radio Link Availability

Channel charting (CC) has become a key technology for RF-based localization, enabling unsupervised radio fingerprinting, even in non line of sight scenarios, with a minimum of reference position labels. However, most CC models assume fixed-size inputs, such as a constant number of antennas or channel measurements. In practical systems, antennas may fail, signals may be blocked, or antenna sets may change during handovers, making fixed-input architectures fragile. Existing radio-fingerprinting approaches address this by training separate models for each antenna configuration, but the resulting training effort scales prohibitively with the array size. We propose Adaptive Positioning (AdaPos), a CC architecture that natively handles variable numbers of channel measurements. AdaPos combines convolutional feature extraction with a transformer-based encoder using learnable antenna identifiers and self-attention to fuse arbitrary subsets of CSI inputs. Experiments on two public real-world datasets (SISO and MIMO) show that AdaPos maintains state-of-the-art accuracy under missing-antenna conditions and replaces roughly 57 configuration-specific models with a single unified model. With AdaPos and our novel training strategies, we provide resilience to both individual antenna failures and full-array outages.

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arXiv2025

Simplicity is Key: An Unsupervised Pretraining Approach for Sparse Radio Channels

Unsupervised representation learning for wireless channel state information (CSI)reduces reliance on labeled data, thereby lowering annotation costs, and often improves performance on downstream tasks. However, state-of-the-art approaches take little or no account of domain-specific knowledge, forcing the model to learn well-known concepts solely from data. We introduce Sparse pretrained Radio Transformer (SpaRTran), a hybrid method based on the concept of compressed sensing for wireless channels. In contrast to existing work, SpaRTran builds around a wireless channel model that constrains the optimization procedure to physically meaningful solutions and induces a strong inductive bias. Compared to the state of the art, SpaRTran cuts positioning error by up to 28% and increases top-1 codebook selection accuracy for beamforming by 26 percentage points. Our results show that capturing the sparse nature of radio propagation as an unsupervised learning objective improves performance for network optimization and radio-localization tasks.

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arXiv2025

Passive Channel Charting: Locating Passive Targets using a UWB Mesh

Fingerprint-based passive localization enables high localization accuracy using low-cost UWB IoT radio sensors. However, fingerprinting demands extensive effort for data acquisition. The concept of channel charting reduces this effort by modeling and projecting the manifold of \ac{csi} onto a 2D coordinate space. So far, researchers only applied this concept to active radio localization, where a mobile device intentionally and actively emits a specific signal. In this paper, we apply channel charting to passive localization. We use a pedestrian dead reckoning (PDR) system to estimate a target's velocity and derive a distance matrix from it. We then use this matrix to learn a distance-preserving embedding in 2D space, which serves as a fingerprinting model. In our experiments, we deploy six nodes in a fully connected ultra-wideband (UWB) mesh network to show that our method achieves high localization accuracy, with an average error of just 0.24\,m, even when we train and test on different targets.

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arXiv2024

Radio Foundation Models: Pre-training Transformers for 5G-based Indoor Localization

Artificial Intelligence (AI)-based radio fingerprinting (FP) outperforms classic localization methods in propagation environments with strong multipath effects. However, the model and data orchestration of FP are time-consuming and costly, as it requires many reference positions and extensive measurement campaigns for each environment. Instead, modern unsupervised and self-supervised learning schemes require less reference data for localization, but either their accuracy is low or they require additional sensor information, rendering them impractical. In this paper we propose a self-supervised learning framework that pre-trains a general transformer (TF) neural network on 5G channel measurements that we collect on-the-fly without expensive equipment. Our novel pretext task randomly masks and drops input information to learn to reconstruct it. So, it implicitly learns the spatiotemporal patterns and information of the propagation environment that enable FP-based localization. Most interestingly, when we optimize this pre-trained model for localization in a given environment, it achieves the accuracy of state-of-the-art methods but requires ten times less reference data and significantly reduces the time from training to operation.

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