Kongress- und Tagungsbeiträge

By subsampling optimally in the spatial and temporal domains, ultrasound imaging can achieve high performance, while also accelerating data acquisition and reducing storage requirements. We study the design of experiment problem that attempts to find an optimal choice of the subsampling patterns, leading to a non-convex combinatorial optimization problem. Recently, deep learning was shown to provide a feasible approach for solving such problems efficiently by virtue of the softmax function as a differentiable approximation of the one-hot encoded subsampling vectors. We incorporate softmax neural networks into information theory-based and task-based algorithms, respectively, to design optimal subsampling matrices in Full Matrix Capture (FMC) measurements predicated on compressed sensing theory.

To efficiently extract estimates about the propagation behavior of electromagnetic waves in a radio environment it is common to invoke the narrowband-assumption. It essentially states that the relative bandwidth of the measurement system is so low that the frequency response of a single propagation path only depends on it Time-of-Flight and the response of the measurement device can be calibrated independently of the measured channel. Recent advances into higher relative bandwidths and antenna arrays with larger spatial aperture render this assumption less likely to be satisfied, which leads to a model mismatch during estimation. In this case estimates are inherently biased and have a special statistical behavior. This behavior can be captured by the so-called Misspecified Cramér-Rao Bound, which formulates a lower bound for the variance of estimates that are biased due to model mismatch. We analyze this bound in contrast to the traditional Cramér-Rao Bound and show the shortcomings in the setting of joint ToF-DoA estimation in the mmWave spectrum. The conducted numerical studies also show that planar array geometries inherently suffer from violation of the narrowband assumption irrespective of the individual elements' frequency response, whereas circular structures show it to a lesser degree.

User positioning with deep learning (DL) models based on channel state information (CSI) fingerprints, e.g., obtained at a base station (BS), has emerged as a promising technology. Related prior works generally assume a CSI fingerprint with multiple spatial dimensions (i.e antennas or beams) at the BS but only a single spatial dimension at the user equipment (UE). However, a UE may be equipped with multiple antennas or may need to perform beamforming, e.g., to support transmissions at higher frequencies. In this work we consider user positioning with DL models based on uplink beamformed CSI fingerprints considering multiple spatial dimensions at both the BS and the UE. By considering a single or multiple beams at the BS and UE, the use of different CSI fingerprints is proposed. The positioning accuracy achieved with the different beamformed CSI fingerprints is evaluated and compared. The different orientation during training and UE deployment is also considered. In addition, we also consider the positioning of UEs with different spatial capabilities, i.e. with different number of beams. This work provides valuable insights into the design of wireless positioning with CSI fingerprints considering multiple spatial dimensions at both the BS and UE.

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 distributed infrastructure, multisensor uplink and downlink, or meshed sidelink access. In this way, ISAC develops into a MS-MIMO (multisensor multiple input multiple output) network which constitutes a distributed MIMO radar network. Multisensor link coordination and synchronization are becoming crucial. Many multisensor access and signaling techniques find their communication counterpart in multiuser MIMO and cooperative multilink communications (CoMP) and can be adopted from there.

We present our new scalable multi-channel and multi-node sounder, the ILMSound G3 . It is configurable in terms of the number of switched Tx and parallel Rx nodes. The basic structures of the Tx and Rx nodes are given with consideration of the measurement system requirements.The ILMSound G3 is validated via a proof of concept measurement at 2.53 GHz in an urban environment. The system was configured as a sounder with two Tx nodes and one Rx node, which records two MIMO links simultaneously. Each node uses an antenna array. The transmitters were moved by cars whereas the receiver was elevated 20 m above the ground by a lifting platform acting as a base station. The used system configuration results in a snapshot rate of approx. 0.5 kHz, which covers the expected time variance of the chosen environment.The measurement results were analyzed using HRPE, providing a parametric description of the specular propagation paths of the radio channel per measurement link. Each estimated path is described by its directions of departure and arrival, delay, Doppler shift, and fully polarimetric complex path weights.

Due to increasingly complex and automated manufacturing processes, the demands on the control parameters of these processes are also increasing. One parameter is the fill quantity of, e.g., liquids in production plants, whose precise determination is of ever-growing importance. Up to now, the exact level of determination under difficult conditions, such as high ambient temperatures, has been a particular challenge. This paper demonstrates a novel method by which an M-sequence UWB radar can determine levels of low-permittivity materials in small metal containers. For this purpose, hot melt is used as an example. Thus, the influence of large temperature differences on the long-term stability of level measurement can also be investigated. The measurements show that the level of hot melt can be measured to be long-term stable with an accuracy of better than 3 mm. Furthermore, the precise determination of the empty state is highly important for many applications. For this reason, this paper shows a method for determining the empty state without complex calibration procedures. For the empty level indication, an accuracy of up to 0.5 mm could be achieved for molten hot glue and 3% of the tank volume, independent of the shape or aggregate state of the medium.

In satellite communications, it is becoming challenging to provide the tracking performance which is required for Non-Geostationary Orbit (NGSO) constellations with the traditional Satellite Communications (SatCom) On The Move (SOTM) terminal structure which employs bulky parabolic antennas. On the other hand, in terrestrial networks, the single omnidirectional communication with User Equipment (UE) does not provide enough throughput to fulfill the need for higher speed connections. As a consequence, manufacturers started to invest in developing new terminals which use phased array antennas to enable beamforming to increase the directivity and null the interference in terrestrial networks and to provide rapid tracking performance as well as seamless handovers in SOTM. However, this generates new challenge as these antennas change beam patterns depending on the beam steering angle. It is not trivial to evaluate the performance of beamforming antennas since the measurement of the high number of beam patterns that the phased array can form in all directions is time consuming. In this paper, we propose a methodology to measure a large number of beam patterns of a phased array antenna in a more time efficient approach compared to traditional antenna measurement methods. The measured patterns can be used to evaluate the antenna performance and capabilities in different conditions and verify the terminal ability to fulfill the requirements specified by the standards.

This paper presents an experimental measurement platform for the research and development of unmanned aerial vehicles (UAVs) localization algorithms using radio emission and reflectivity. We propose a cost-effective, flexible testbed made from commercial off-the-shelf (COTS) devices to allow academic research regarding the upcoming integration of UAV surveillance in existing mobile radio networks in terms of integrated sensing and communication (ISAC). The system enables nanosecond-level synchronization accuracy and centimeter-level positioning accuracy for multiple distributed sensor nodes and a mobile UAV-mounted node. Results from a real-world measurement in a 16 km^2 urban area demonstrate the system’s performance with both emitter localization as well as with the radar setup.

The mmWave and sub-THz bands are foreseen as candidates to achieve the data-rate demands in the beyond 5G and 6G wireless communication networks. The co-existence of multiple radio interfaces at several bands enables data fusion and the utilization of the similarities and differences on propagation and system properties for communication and sensing applications. MmWave radio interfaces rely on directive beams that require high training overhead for beam steering. Sensors in the network infrastructure and co-located radio interfaces at sub-6 GHz and mmWave can be used to assist the beam-forming process at mmWave. In the present paper we investigate the performance of multi-band assisted beam-forming in an industrial environment. We empirically demonstrate from real-world measurements that even in NLOS, the direction of the beams estimated at sub-6 GHz can be used to established a link at mmWave.

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.

Due to increasingly complex and automated manufacturing processes, the demands on the control parameters of these processes are also increasing. In many applications, such a parameter is the fill quantity, whose precise determination is of ever growing importance. This paper shows with which accuracy and precision an M-sequence ultra-wideband radar can determine levels in small metallic and non-metallic containers with contact-based and contactless measurements. First, the principle of level measurement using guided wave radar is explained and the measurement setup is described. Afterward, the measurement results are shown and discussed. The measurements show that the level can be measured with an accuracy of better than 0.5 mm. In addition, level fluctuations can be detected with a precision of 3 μm. Based on the results of the guided wave radar, the possibilities of volumetric contactless measurement using an electrically small patch antenna are discussed. A particular challenge in contactless level measurement is the high number of multipath components, which strongly influence the accuracy. In addition, there are near-field effects when measuring close to the antenna. Exploiting these near-field effects, an additional method to accurately determine the full state of the container is investigated.

With the fast development of wireless devices, over-the-air (OTA) testing is becoming the preferred method among developers and manufacturers of wireless equipment. The ability to recreate a scenario under controllable and repeatable conditions keeps the method under constant development, providing new features that increase the realism during the tests. A recent proof of that is the integration of 3D wave field synthesis (3DWFS) to OTA testing, which becomes a significant step to accurately emulate wireless scenarios within a controlled environment.In this context, this contribution improves the OTA system calibration for 3DWFS; efficiently increasing the emulation quality of electromagnetic plane waves impinging from any angular position within an anechoic chamber. In fact, this enhancement implicitly delivers a new method for accurate estimation of the antenna radiation pattern in 3D. This is not only a highly demanded application among antenna manufacturers but in this case also proves the validity of the results and consolidates the integration of 3DWFS to OTA testing.

Despite the growing interest in many fields, spatio-temporal (ST) interpolation remains challenging. Given ST nonstationary data distributed sparsely and irregularly over space, our objective is to obtain an equidistant representation of the region of interest (ROI). For this reason, an equidistant grid is defined within the ROI, where the available time series data are arranged, and the time series of the unobserved points are interpolated. Aiming to maintain the interpretability of the whole process while offering flexibility and fast execution, this work presents a ST interpolation frame-work which combines a statistical technique with deep learning. Our framework is generic and not confined to a specific application, which also provides the prediction confidence. To evaluate its validity, this framework is applied to ultrasound nondestructive testing (UT) data as an example. After the training with synthetic UT data sets, our framework is shown to yield accurate predictions when applied to measured UT data.

In the present paper we evaluate scattering properties of fruits measured with a short-range Ultra-Wideband radar. This is part of our investigation how effectively such a radar can be used to infer information such as fruit biomass or ripeness in an agricultural environment. The covered frequency band spans from 1.4 to 5.6 GHz. We analyze measured impulse responses of a watermelon, a grapefruit, and an apple with respect to a dependency on the distance between radar and fruit and the observation angle i.e., rotation of the fruit. Measurements are performed under laboratory conditions, however, we analyze the data considering a pre-harvest analysis on a field. It becomes apparent that an analysis of the dispersed dominant reflection of the peel is most promising. Due to the natural growth and hence anisotropy of the fruits, we conclude to average over multiple monostatic observation angles to reduce the natural variations of e.g. the scattered power.

In this paper, we present the ray tracing (RT) simulation in the 3D model of one highly dense clutter industrial hall, which is scanned by laser scanner and reconstructed based on accurate point cloud. The whole processing chain from the scanning of the physical environment to running the simulation is presented in detail. To validate the simulation results, the synthetic channel characteristics and large-scale parameters, including delay spread (DS), angular spread (AS) and path loss (PL), are compared with those obtained from channel sounding measurement in both LOS and NLOS cases, at 6.75 GHz, 30 GHz and 60 GHz. The simulation results show that some scatters are significant in all bands and may be well identified and tracked. This indicates that our target to generate a deterministic channel model or a hybrid channel model at multi-band for industrial scenario may be possible.

A LTCC patch antenna array was designed and fabricated in Low Temperature Co-fired Ceramic (LTCC) multilayer technology using picosecond laser structuring for precise manufacturing of embedded air cavities and feeding structures, which are beneficial for the antenna RF performance. Moreover, free-standing feeding vias in the embedded air cavities were successfully implemented in LTCC technology that enable a very simple and low-loss feed at the antenna ports. The 2x2 dual-polarized patch antenna array with embedded air cavities and transitions was first characterized by reflection measurements. The measured center frequency of the antenna is about 29.5 GHz and a bandwidth of nearly 1 GHz was achieved.

The use of service robots has become much more relevant in industry and retail in recent years. To be of best possible benefit to humans, an autonomous motion ability of the service robots is of central importance. This requires that the robot can perceive its surroundings as precisely as possible by using different sensors and their fusion. In contrast to optical sensors, an UWB sensor can, for example, detect small and almost invisible objects such as glass panes with high range resolution. In this paper, a signal processing chain for detection of the environment with M-sequence UWB sensors is proposed. The proposed chain includes pulse reconstruction based on calibration measurements, background subtraction, object detection based on CFAR techniques, and multi-target tracking based on the Kalman filter and the nearest-neighbor approach.

The procedure of estimating the angular dependent delay of an Ultra Wideband (UWB) antenna, directly in frequency domain, is presented. It is shown that in this approach any occurrences that can lead to inaccurate results, such as the windowing effect, can be entirely avoided. It alleviates the need to design a specific pulse, since the transfer function can be obtained by a frequency sweep. Moreover, polarization mismatch is accounted in the analysis. To this end, an UWB monopole antenna is designed and it is simulated with time and frequency domain solvers. The simulation results demonstrate that only frequency domain analysis is sufficient for the delay estimation. The outcome of this work provides a clear procedure for obtaining the angular dependent delay of an antenna in an accurate way.

Due to increasingly complex and automated manufacturing processes, the demands on the control parameters of these processes are also increasing. One parameter is the fill quantity of e.g. liquids in production plants, whose precise determination is of ever growing importance. Up to now, the exact level determination under difficult conditions, such as high ambient temperatures, has been a particular challenge. This paper demonstrates a novel method by which an M-sequence UWB radar can determine levels of low-permittivity materials in small metal containers. For this purpose, hot melt is used as an example. Thus, the influence of large temperature differences on the long-term stability of level measurement can also be investigated. The measurements show that the level of hot melt can be measured long-term stable with an accuracy of better than 3 mm.

In this paper, we investigate the gain switching performance of the software-defined radio (SDR) universal software radio peripheral (USRP) X310 with the TwinRX daughterboard and propose a method to optimize it for fast transitions. This receiver uses a complex structure of two switchable amplifiers and two digital step attenuators to realize the wide gain setting range of up to 92 dB. Our measurements of the settling time show that the bulk of transitions between two gain settings take below 1.5 µs, but some require more than 50 µs, caused by the power-up of the switchable amplifiers. Based on this knowledge, we propose a modified switching regime, in which the amplifiers are not toggled in time-critical phases, e.g., during a running measurement, so that a transition time of 1.5 µs can be reliably realized. Thus, a gain switching range of 62 dB is achieved, within which, for example, a fast automatic gain control can be realized.

Ultrasound Computed Tomography (UCT) is challenging due to phenomena such as strong refraction, multiple scattering, and mode conversion. In NDT, large speed of sound contrasts lead to strong artifacts if such phenomena are not modeled correctly; however, enhanced models are computationally expensive. In this work, a two-step framework for Compressed UCT based on the integral approach to the solution of the Helmholtz equation is presented. It comprises a physically motivated forward step and an imaging step that solves a suitable inverse problem. Multiple scattering is accounted for through the use of Neumann series. Convergence problems of Neumann series in high contrast settings are addressed via Padé approximants. Compressed sensing is employed to reduce the computational complexity of the reconstruction procedure by reducing data volumes directly at the measurement step, avoiding redundancy in the data and allowing the ability to steer the admissible computational effort at the expense of reconstruction quality. The proposed method is shown to yield high quality reconstructions under heavy subsampling in the frequency and spatial domains.

Energy efficiency has recently drawn significant attention in diverse applications, such as vehicle-to-everything (V2X) communications. This paper investigates a joint energy efficiency and sum-rate maximization problem in autonomous resource selection in the 5G new radio (NR) vehicular communication while catering for reliability and latency requirements. An autonomous resource allocation problem is formulated as a ratio of sum-rate to energy consumption, where the objective is to maximize the total energy efficiency of power-saving users subject to the reliability and latency requirements. The energy efficiency problem is a mixed-integer programming optimization problem that is computationally complex when the number of users increases. Therefore, a heuristic algorithm, density of traffic-based resource allocation (DeTRA), is proposed to solve the problem. The traffic density per traffic type is considered a metric to split the radio resources and trigger an appropriate resource selection strategy. The simulation results show that the proposed algorithm outperforms the existing schemes in terms of energy efficiency.

This paper describes a field-programmable gate array (FPGA)-based implementation of a switched multiple input multiple output (MIMO) channel sounder realization by use of software-defined radio (SDR) hardware (NI USRP-2954, X310 series with UBX 160 daughterboard). Essential basic functionalities are the synchronous switching of the antenna elements at both link ends and an integrated automatic gain control (AGC) at the receiver. To meet the high timing requirements in the switched MIMO setup, an FPGA-based AGC is introduced. The detector of the AGC is based on a sample-by-sample calculation of the average and maximum of the I/Q samples within a sliding window. The used SDR hardware allows a minimum AGC update interval of approx. 14 [my]s and a timing accuracy of the antenna switching of 5 ns. Our setup demonstrates the applicability of state-of-the-art SDRs as a sounding system for continuous acquisition of the time-variant, directional mobile radio channel.

The statistical study of target detectability is crucial in radar system design. In this regard, signal-to-noise ratio (SNR) of the received signal at the radars receiver is a key parameter and therefore every influence on SNR should be taken into account. One of the main influences is the fluctuation of the target scattering/reflectivity to the incident electromagnetic wave. Consequently, the target fluctuation models and their accuracy become important. For monostatic radar system, various models can be found in the literature. In contrast, this paper studies a case of vehicular sensing scenario with multistatic configuration for an urban crossroad and proposes a modelling method of the target fluctuation by taking into account the target information, such as size, shape and composed materials, and the wave information, such as polarizations and aspect angles. Furthermore, the derived target fluctuation models are then applied directly to the formulation of the probability of detection for multistatic radar system. These probabilities of detection are also compared to that of monostatic radar system.

With this paper, we present cost-effective techniques to characterize UHF RFID performance with regards to small load carriers (SLC). A self-developed turntable-positioner is used to measure the antenna pattern of tags attached to empty SLCs, as well as SLCs filled with scrap metal. With the tag-positioner we presented in a recent publication, read range degradation of various tags near dissipative and conductive electro static discharge (ESD) protective material was investigated. We also measured the sensitivity of around 80 different tags in front of an SLC made of ESD protective material and compared their performance to operation in free-space propagation. To conduct the measurements themselves, we use commercial off-the-shelf readers like the R420 from Impinj, which we control via low level reader protocol (LLRP) commands, implemented in Python.

This contribution proposes modeling methods for MIMO-TDL channel models under new evaluation method, which is non-WSSUS. V2X channels cannot be assumed to follow the WSSUS assumption due to high inherent mobility. It can be extended to non-WSSUS by employing an ON/OFF process (persistence process). An algorithm to select taps and estimate their parameters from measured data is illustrated. Meanwhile, the slow and fast fading is separated from received signal to assess the fading distribution of taps. The analysis investigates correlation coefficient among taps persistence process, as well as, among taps amplitude. Additionally, an approach is discussed to develop a time varying MIMO-TDL model, whose parameters are varying over time.

The characterization of propagation at mm-waves and THz is obtaining relevance since they are expected to be the frequency bands of the future wireless generations. Hence, measurements conducted all around the world with different equipments need to be comparable by establishing common methodologies and best-practice guidelines. As an example, all measurements are affected by noise, but its estimation and removal are not standardized. Therefore, in the present paper we define a methodology on the estimation of the noise floor and the cut-off margin for its subsequent removal. We firstly analyse the influence of the channel sounder noise floor on the estimation of different propagation parameters directly from the measured channel impulse responses (CIRs), showing the need of its removal before any processing of the measurements. Later, we introduce an algorithm to estimate the noise floor in the presence of signal, and the cut-off margin. Finally, we evaluate its performance empirically with measurements.

In this paper we present the latest results of a research program on the use of ground penetrating radar and machine learning-based algorithms for humanitarian demining in Colombia. We describe the experimental setup developed, which includes a polarimetric GPR-UWB unit, a radar positioning system, and a control unit. We describe as well a soil characterization campaign and an IED classification technique based on Support Vector Machine.

A vehicle to X (V2X) propagation channel corresponds to a wideband channel that means a receiver (Rx) able to resolve multipath components. In contrast to a narrow band channel, multipath components are indistinguishable at the receiver and seen as a single composite signal, thus fading approximation has been assumed as the Rayleigh fading. It is interesting to see how the fading is characterized in V2X applications because the channel corresponds to the wideband and it can not be assumed to follow wide sense stationary uncorrelated scattering (WSSUS) assumption due to inherent mobility. The aim of this paper is to characterize and study slow fading and fast fading variations of each tap, which is selected from measurement data. This investigation pertains to three various scenarios, which are omnidirectional antenna, two antennas faced directly to each other and two antennas faced away from each other. In each scenario, LOS and two different NLOS categories are studied.

This paper provides an overview of recent developments in Joint Communication and Radar Sensing (JCRS) systems and networks. Our discussion will focus on radio system and network architectures that can be used for JCRS and on approaches to select, generate and process waveforms suitable for both communication and sensing. In addition, advantages and challenges of assembling a network out of several JCRS nodes are addressed. Finally, we give an outlook to possible applications of JCRS networks, which we see in autonomous driving and low altitude air traffic control.

A millimeter wave agile multi-beam front-end with an integrated 4 by 1 antenna array for 5th generation wireless communications is analyzed by antenna and digital communication measurements. The front-end core comprises a compact low-temperature co-fired ceramic multilayer module with a foot-print area of 74 mm × 74 mm. An over-the-air gain of 50.4 dB and a noise figure of 4.9 dB were measured. Link measurements with broadband single-carrier QAM and OFDM signals result in a data rate of 3.2 Gb/s for 400 MHz bandwidth SC-256QAM modulation with 1.2% error vector magnitude of the front-end determined by a detailed analysis.

As one of the key features in 5G network, Millimeter wave (mmWave) technology can provide the ultra-wide bandwidth to support higher data rate. However, for high frequency band, mmWave signal still suffers from the high pathloss, the multipath fading and the signal blockage issue, especially in the indoor environment. For different application scenarios, the channel conditions and quality of services (QoS) are quite different. Therefore, it is essential to investigate the impact of the mmWave channel on the system performance. This paper investigates and measures the performance of a 60GHz mmWave module that is exploited for the downlink and uplink high data rate transmission in the Internet of Radio-Light (IoRL) project. The coverage area and the throughput of the mmWave module is estimated by measuring the error vector magnitude (EVM) of received signals with different transmitter (TX) and receiver (RX) angles and at different locations in a laboratory. In this paper, the measurement environment and system setup are introduced. After that, the waveform design for the measurement is also discussed. The measurement results show that this 60GHz mmWave module can provide an acceptable performance only in some cases, which restricts its application scenarios.

Automotive radar systems are indispensable for advanced driver assistance systems and traffic safety. Besides existing monostatic radar techniques, bi-static radar sensing like passive coherent location offers additional options to improve the radar visibility of vulnerable road users. Regarding the testing and evaluation of signal processing algorithms including parameter estimation, it is essential to provide electromagnetically shielded and reproducible measurement conditions, in addition to field tests in real traffic scenarios. This study describes the possibility to emulate relevant performance parameters for bi-static radar scenarios in the frequency range from 1 GHz to 6 GHz in a metal-shielded semi-anechoic chamber. Of special interest are the bi-static angle between transmitter, target, and receiver, and the resulting bi-static Doppler frequencies of a realistic vehicular traffic scenario. According to the concept of cooperative passive coherent location, Doppler scattering measurements are presented and compared to electromagnetic simulations. The authors find promising agreement between measured and ground truth data in the delay-Doppler spectrum.

We propose a compact hardware architecture for measuring sparse channel impulse responses (IR) by extending the M-Sequence ultra-wideband (UWB) measurement principle with the concept of compressed sensing. A channel is excited with a periodic M-sequence and its response signal is observed using a Random Demodulator (RD), which observes pseudo-random linear combinations of the response signal at a rate significantly lower than the measurement bandwidth. The excitation signal and the RD mixing signal are generated from compactly implementable Linear Feedback Shift registers (LFSR) and operated from a common clock. A linear model is derived that allows retrieving an IR from a set of observations using Sparse-Signal-Recovery (SSR). A Matrix-free model implementation is possible due to the choice of synchronous LFSRs as signal generators, resulting in low computational complexity. For validation, real measurement data of a time-variant channel containing multipath components is processed by simulation models of our proposed architecture and the classic M-Sequence method. We show successful IR recovery using our architecture and SSR, outperforming the classic method significantly in terms of IR measurement rate. Compared to the classic method, the proposed architecture allows faster measurements of sparse time-varying channels, resulting in higher Doppler tolerance without increasing hardware or data stream complexity.

This paper introduces a testbed to characterize the performance of GPS disciplined oscillators using a GNSS signal simulator and a measurement system based on software-defined radios and digital signal processing that allows to examine the stability of up to four devices with sub-nanosecond precision, time-coherently for 1 PPS and 10 MHz signals, in a controllable laboratory environment with reproducible and adjustable GNSS signal settings over a long-term observation period. To demonstrate the effectiveness of this method, four devices of one specific low-cost GPSDO type available to the authors are characterized and compared in terms of their positional accuracy and their time and frequency stability in steady state and during stabilization.

Traditional black box clock skew models are either the power-law noise model or a more recent approach based on auto-regressive (AR) filters. Unfortunately, neither algorithm can accurately model short and long term skew due to limited degrees of freedom or stability constraints. We propose a novel model that employs the current AR algorithm recursively with appropriate pre- and post-processing to achieve numeric stability and accurate reproduction of short and long term effects. The model coefficients are derived from a measured skew signal, with the model output matching an exemplary original signal closely in terms of Allan variance and time-domain behavior.

Structure-borne acoustic emission (AE) measurement shows major advantages regarding quality assurance and process control in industrial applications. In this paper, laser beam welding of steel and aluminum was carried out under varying process parameters (welding speed, focal position) in order to provide data by means of structure-borne AE and simultaneously high-speed video recordings. The analysis is based on conventionally (e.g. filtering, autocorrelation, spectrograms) as well as machine learning methods (convolutional neural nets) and showed promising results with respect to the use of structure-borne AE for process monitoring using the example of spatter formation.

Ultra-wideband active array imaging has proven extremely valuable for biomedical diagnostics. At the same time, the underlying technologies have achieved a high degree of maturity. Across institutions, we have merged our expertise in M-sequence radar systems, antennas, low-noise and high-power circuitry, to devise an UWB MIMO radar system for breast tumour localisation. Recent progress in UWB imaging and hardware for active antenna arrays is presented.