arXiv subjects

Reiner S. Thomä

Publications and source records attributed to Reiner S. Thomä.

At least 19 recordsLinked to original sources

arXiv2026

BIRA: A Spherical Bistatic Radar Reflectivity Measurement System

The upcoming 6G mobile communication standard will offer a revolutionary new feature: Integrated sensing and communication (ISAC) reuses mobile communication signals to realize multi-static radar for various applications including localization. Consequently, applied ISAC propagation research necessitates to evolve from classical monostatic radar cross section (RCS) measurement of static targets on to bistatic radar reflectivity characterization of dynamic objects. Here, we introduce our Bistatic Radar (BIRA) measurement facility for independent spherical positioning of two probes with sub-millimeter accuracy on a diameter of up to 7 m and with almost continuous frequency coverage from 0.7 up to 260 GHz. Currently, BIRA is the only bistatic measurement facility capable of unrestricted ISAC research: In addition to vector network analysis, it employs advanced wideband transceiver technology with an instantaneous bandwidth of up to 4 GHz. These transceivers grant BIRA the unique capability to characterize dynamic targets in both Doppler and range, while also significantly accelerating measurements on static objects. Additionally, the installation is capable of spherical near-field antenna measurements over these wide frequency ranges.

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arXiv2026

Wideband Antenna Deconvolution for Bistatic Millimeter Wave Radar Reflectivity Measurements

Bistatic radar measurements offer unique spatial diversity and enhanced target characterization capabilities, rendering them increasingly vital for contemporary sensing application research. The reliability of such measurements is contingent upon precise system and antenna calibration. The prevailing technique is the substitution method, which involves the use of known reference objects. We propose an over-the-air calibration algorithm for spherical bistatic measurement systems. Our method is both significantly simpler and twice as fast as existing algorithms. The application of our technique to reflectivity measurements of a metal sphere from 76 to 81 GHz demonstrates a dynamic range enhancement of up to 40 dB when compared with uncalibrated data. A comparison with simulation data demonstrates a high degree of agreement between measurement and simulation.

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arXiv2026

Performance Comparison of Joint Delay-Doppler Estimation Algorithms

Integrated sensing and communications (ISAC), radar, and beamforming require real-time, high-resolution estimation algorithms to determine delay-Doppler values of specular paths within the wireless propagation channel. Our contribution is the measurement-based performance comparison of the delay-Doppler estimation between three different algorithms, comprising maximum likelihood (ML), convolutional neural network (CNN), and constant false alarm rate (CFAR) approaches. We apply these algorithms to publicly available channel data which includes two spherical targets with analytically describable delay-Doppler parameters. The comparison of the three algorithms features the target detection rate, root mean squared errors (RMSEs) of the delay-Doppler estimates, and a runtime analysis. Notably, all three algorithms demonstrate similar parameter estimation capabilities in bi-static scenarios, achieving target detection probabilities of up to 80%. Conversely, forward and backward scattering conditions pose a problem to the estimation due to strong line-of-sight (LoS) contribution, reducing the corresponding detection probability down to 0%.

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arXiv2026

Background Subtraction with Drift Correction for Bistatic Radar Reflectivity Measurements

Fundamental research on bistatic radar reflectivity is highly relevant, e.g., to the upcoming mobile communication standard 6G, which includes integrated sensing and communication (ISAC). We introduce a model for correcting instrumentation drift during bistatic radar measurements in anechoic chambers. Usually, background subtraction is applied with the goal to yield the target reflection signal as best as possible while coherently subtracting all signals which were present in both the foreground and background measurement. However, even slight incoherences between the foreground and background measurement process deteriorate the result. We analyze these effects in real measurements in the frequency range 2-18 GHz, taken with the Bistatic Radar (BIRA) measurement facility at TU Ilmenau. Applying our proposed drift correction model, we demonstrate up to 40 dB improvement for the removal of direct line-of-sight antenna crosstalk over the state of the art.

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arXiv2025

Geometry-Based Drift Compensation for Distributed Channel Sounding Measurements in Dynamic Drone Scenarios

Measured impulse responses obtained from a dynamic unmanned aerial vehicle (UAV) channel sounding system exhibit effects attributable to time-varying carrier frequency offset (CFO) and sampling frequency offset (SFO). To correct the recorded data in post-processing, we extend existing geometry-based drift compensation algorithms by an explicit line-of-sight (LoS) determination, combining a symbol-wise high-resolution parameter estimation (HRPE) in delay with a Kalman filter. This proposed extension facilitates the removal of rapidly varying synchronization mismatches from channel sounding measurements in rich multipath propagation scenarios. Furthermore, we propose using the relative residual power after subtraction of estimated multipath components as a metric for ground-truth-independent comparison of post-processing synchronization methods for recorded channel sounding data. The application of the proposed procedure shows that our approach outperforms existing post-processing compensation algorithms, reducing the relative residual power by more than 5 dB and the delay-Doppler estimate root mean square errors (RMSEs) of a passive UAV target by approximately 60 %.

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arXiv2025

Modeling Micro-Doppler Signature of Multi-Propeller Drones in Distributed ISAC

Integrated Sensing and Communication (ISAC) will be one key feature of future 6G networks, enabling simultaneous communication and radar sensing. The radar sensing geometry of ISAC will be multistatic since that corresponds to the common distributed structure of a mobile communication network. Within this framework, micro-Doppler analysis plays a vital role in classifying targets based on their micromotions, such as rotating propellers, vibration, or moving limbs. However, research on bistatic micro-Doppler effects, particularly in ISAC systems utilizing OFDM waveforms, remains limited. Existing methods, including electromagnetic simulations, often lack scalability for generating the large datasets required to train machine learning algorithms. To address this gap, this work introduces an OFDM-based bistatic micro-Doppler model for multi-propeller drones. The proposed model adapts the classic thin-wire model to include bistatic sensing configuration with an OFDM-like signal. Then, it extends further by incorporating multiple propellers and integrating the reflectivity of the drone's static parts. Measurements were performed to collect ground truth data for verification of the proposed model. Validation results show that the model generates micro-Doppler signatures closely resembling those obtained from measurements, demonstrating its potential as a tool for data generation. In addition, it offers a comprehensive approach to analyzing bistatic micro-Doppler effects.

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arXiv2025

Modelling Micro-Doppler Signature of Drone Propellers in Distributed ISAC

Integrated Sensing and Communication (ISAC) comprises detection and analysis of non-cooperative targets by exploiting the resources of the mobile radio system. In this context, micro-Doppler is of great importance for target classification, in order to distinguish objects with local movements. For developing algorithms for target classification, it is necessary to have a large amount of target signatures. Aiming to generate these data, this paper proposes a mathematical model for the micro-Doppler of drone rotating propellers, and validate the proposed model by comparing it to measured micro-Doppler. Results show that the proposed mathematical model can generate micro-Doppler data very similar to those from measurement data.

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arXiv2025

Real-Time Sounding in ISAC networks: Design and Implementation of a Multi-Node Testbed with Synchronized Airborne and Ground-Based Sensors

As integrated sensing and communication (ISAC) capabilities become more prevalent in the mobile 6G radio landscape, there is a substantial opportunity to enhance situational awareness across diverse applications through multi-static radar sensing within meshed ISAC networks. To facilitate the development and testing of detection and localization algorithms across diverse scenarios, this paper introduces a synchronized distributed channel sounding testbed with airborne and ground-based multi-channel transceiver nodes with centimeter-level positioning accuracy enabled by real-time kinematic (RTK) and inertial navigation system (INS) data. Our modular experimental measurement system is designed to include stationary sensor nodes and light-weight to medium-weight mobile nodes deployable on unmanned aerial vehicles (UAVs), cars, pedestrians, and cyclists. Utilizing commercial off-the-shelf (COTS) hardware, specifically software defined radios (SDRs), the testbed encourages reproducibility in academic research laboratories. We detail the individual modules and integration steps required to achieve the specified performance. The testbed's capabilities are validated through a real-world measurement campaign, including stationary and flying sensor nodes, aimed at detecting radar targets such as vertical take-off and landing (VTOL) aircrafts, small hexacopters, cars and vulnerable road users (VRUs) in air-to-air (A2A) and air-to-ground (A2G) scenarios.

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arXiv2025

Bistatic Micro-Doppler Analysis of a Vertical Takeoff and Landing (VTOL) Drone in ICAS Framework

Integrated Communication and Sensing (ICAS) is a key technology that enables sensing functionalities within the next-generation mobile communication (6G). Joint design and optimization of both functionalities could allow coexistence, therefore it advances toward joint signal processing and using the same hardware platform and common spectrum. Contributing to ICAS sensing, this paper presents the measurement and analysis of the micro-Doppler signature of Vertical Takeoff and Landing (VTOL) drones. Measurement is performed with an OFDM-like communication signal and bistatic constellation, which is a typical case in ICAS scenarios. This work shows that micro-Doppler signatures can be used to precisely distinguish flight modes, such as take-off, landing, hovering, transition, and cruising.

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arXiv2024

Deep Learning based Positioning with Multi-task Learning and Uncertainty-based Fusion

Deep learning (DL) methods have been shown to improve the performance of several use cases for the fifth-generation (5G) New radio (NR) air interface. In this paper we investigate user equipment (UE) positioning using the channel state information (CSI) fingerprints between a UE and multiple base stations (BSs). In such a setup, we consider two different fusion techniques: early and late fusion. With early fusion, a single DL model can be trained for UE positioning by combining the CSI fingerprints of the multiple BSs as input. With late fusion, a separate DL model is trained at each BS using the CSI specific to that BS and the outputs of these individual models are then combined to determine the UE's position. In this work we compare these different fusion techniques and show that fusing the outputs of separate models achieves higher positioning accuracy, especially in a dynamic scenario. We also show that the combination of multiple outputs further benefits from considering the uncertainty of the output of the DL model at each BS. For a more efficient training of the DL model across BSs, we additionally propose a multi-task learning (MTL) scheme by sharing some parameters across the models while jointly training all models. This method, not only improves the accuracy of the individual models, but also of the final combined estimate. Lastly, we evaluate the reliability of the uncertainty estimation to determine which of the fusion methods provides the highest quality of uncertainty estimates.

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arXiv2024

Transfer Learning for CSI-based Positioning with Multi-environment Meta-learning

Utilizing deep learning (DL) techniques for radio-based positioning of user equipment (UE) through channel state information (CSI) fingerprints has demonstrated significant potential. DL models can extract complex characteristics from the CSI fingerprints of a particular environment and accurately predict the position of a UE. Nonetheless, the effectiveness of the DL model trained on CSI fingerprints is highly dependent on the particular training environment, limiting the trained model's applicability across different environments. This paper proposes a novel DL model structure consisting of two parts, where the first part aims at identifying features that are independent from any specific environment, while the second part combines those features in an environment specific way with the goal of positioning. To train such a two-part model, we propose the multi-environment meta-learning (MEML) approach for the first part to facilitate training across various environments, while the second part of the model is trained solely on data from a specific environment. Our findings indicate that employing the MEML approach for initializing the weights of the DL model for a new unseen environment significantly boosts the accuracy of UE positioning in the new target environment as well the reliability of its uncertainty estimation. This method outperforms traditional transfer learning methods, whether direct transfer learning (DTL) between environments or completely training from scratch with data from a new environment. The proposed approach is verified with real measurements for both line-of-sight (LOS) and non-LOS (NLOS) environments.

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arXiv2024

Bistatic Reflectivity and Micro-Doppler Signatures of Drones for Integrated Communication and Sensing

The integration of wireless communication and radar sensing is gaining the interest of researchers from wireless communication and radar societies. Sensing in Integrated Communication and Sensing (ICAS) systems differs from the traditional radar system in the configuration of transmitter-target-receiver, the operating frequency bands, and the transmitting waveform. It is necessary to understand how target electromagnetic signatures behave in this context. Therefore, this paper presents measurements and analysis of two important target signatures, reflectivity and micro-Doppler, for sensing in ICAS. These target signatures are measured in the state-of-the-art measurement system, Bistatische-Radar-Messeinrichtung (BiRa).

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arXiv2024

Sounding-Based Evaluation of Multi-Sensor ISAC Networks for Drone Applications: Measurement and Simulation Perspectives

With the upcoming multitude of commercial and public applications envisioned in the mobile 6G radio landscape using unmanned aerial vehicles (UAVs), integrated sensing and communication (ISAC) plays a key role to enable the detection and localization of passive objects with radar sensing, while optimizing the utilization of scarce resources. To explore the potential of future ISAC architectures with UAVs as mobile nodes in distributed multi-sensor networks, the system's fundamental capability to detect static and dynamic objects that reveal themselves by their bi-static back-scattering needs to be evaluated. Therefore, this paper addresses simulation- and measurement based data acquisition methods to gather knowledge about the bistatic reflectivity of single objects including their Micro-Doppler signature for object identification as well as the influence of multipath propagation in different environments on the localization accuracy and radar tracking performance. We show exemplary results from simulation models, bi-static reflectivity measurements in laboratory environment and real-flight channel sounding experiments in selected scenarios showcasing the potential of synthetic and measured data sets for development and evaluation of ISAC algorithms. The presented measurement data sets are publicly available to encourage the academic RF community to validate future algorithms using realistic scenarios alongside simulations models.

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arXiv2023

Characterization of Multi-Link Propagation and Bistatic Target Reflectivity for Distributed Multi-Sensor ISAC

Integrated sensing and communication (ISAC) qualifies mobile radio systems for detecting and localizing of passive objects by means of radar sensing. Advanced ISAC networks rely on meshed mobile radio access nodes (infrastructure and/or user equipment, resp.) establishing a distributed, multistatic MIMO radar system in which each target reveals itself by its bistatic backscattering. Therefore, characterization of the bistatic reflectivity of targets along their trajectories of movement is of highest importance for ISAC performance prediction. We summarize several challenges in bistatic modeling and measurement of extended, potentially time-variant radar targets. We emphasize the specific challenges arising for distributed (hence multi-link) ISAC networks and compare to the state of the art in propagation modeling for mobile communication.

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arXiv2022

Multi-Environment based Meta-Learning with CSI Fingerprints for Radio Based Positioning

Radio based positioning of a user equipment (UE) based on deep learning (DL) methods using channel state information (CSI) fingerprints have shown promising results. DL models are able to capture complex properties embedded in the CSI about a particular environment and map UE's CSI to the UE's position. However, the CSI fingerprints and the DL models trained on such fingerprints are highly dependent on a particular propagation environment, which generally limits the transfer of knowledge of the DL models from one environment to another. In this paper, we propose a DL model consisting of two parts: the first part aims to learn environment independent features while the second part combines those features depending on the particular environment. To improve transfer learning, we propose a meta learning scheme for training the first part over multiple environments. We show that for positioning in a new environment, initializing a DL model with the meta learned environment independent function achieves higher UE positioning accuracy compared to regular transfer learning from one environment to the new environment, or compared to training the DL model from scratch with only fingerprints from the new environment. Our proposed scheme is able to create an environment independent function which can embed knowledge from multiple environments and more effectively learn from a new environment.

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arXiv2021

Reliable Deep Learning based Localization with CSI Fingerprints and Multiple Base Stations

Deep learning (DL) methods have been recently proposed for user equipment (UE) localization in wireless communication networks, based on the channel state information (CSI) between a UE and each base station (BS) in the uplink. With the CSI from the available BSs, UE localization can be performed in different ways. One the one hand, a single neural network (NN) can be trained for the UE localization by considering the CSI from all the available BSs as one overall fingerprint of the user's location. On the other hand, the CSI at each BS can be used to obtain an estimate of the UE's position with a separate NN at each BS, and then the position estimates of all BSs are combined to obtain an overall estimate of the UE position. In this work, we show that UE localization with the latter approach can achieve a higher positioning accuracy. We propose to consider the uncertainty in the UE localization at each BS, such that overall UE's position is determined by combining the position estimates of the different BSs based on the uncertainty at each BS. With this approach, a more reliable position estimate can be obtained in case of variations in the channel.

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arXiv2021

CSI-Based Localization with CNNs Exploiting Phase Information

In this paper we study the use of the Channel State Information (CSI) as fingerprint inputs of a Convolutional Neural Network (CNN) for localization. We examine whether the CSI can be used as a distinct fingerprint corresponding to a single position by considering the inconsistencies with its raw phase that cause the CSI to be unreliable. We propose two methods to produce reliable fingerprints including the phase information. Furthermore, we examine the structure of the CNN and more specifically the impact of pooling on the positioning performance, and show that pooling over the subcarriers can be more beneficial than over the antennas.

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arXiv2019

Cooperative Passive Coherent Location: A Promising 5G Service to Support Road Safety

5G promises many new vertical service areas beyond simple communication and data transfer. We propose CPCL (cooperative passive coherent location), a distributed MIMO radar service, which can be offered by mobile radio network operators as a service for public user groups. CPCL comes as an inherent part of the radio network and takes advantage of the most important key features proposed for 5G. It extends the well-known idea of passive radar (also known as passive coherent location, PCL) by introducing cooperative principles. These range from cooperative, synchronous radio signaling, and MAC up to radar data fusion on sensor and scenario levels. By using software-defined radio and network paradigms, as well as real-time mobile edge computing facilities intended for 5G, CPCL promises to become a ubiquitous radar service which may be adaptive, reconfigurable, and perhaps cognitive. As CPCL makes double use of radio resources (both in terms of frequency bands and hardware), it can be considered a green technology. Although we introduce the CPCL idea from the viewpoint of vehicle-to-vehicle/infrastructure (V2X) communication, it can definitely also be applied to many other applications in industry, transport, logistics, and for safety and security applications.

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