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An active transmitarray antenna is presented for Ka-band monopulse tracking radar in the 34-36-GHz range. A novel contribution is the application of commercial SiGe-based 5G beamforming chips in a large-scale naval radar array, demonstrating how developments in Ka-band communications can benefit radar technology. Three arrays of 0.77 m diameter each with 24368, 19424, and 7824 radiating elements are presented to compare amplitude-tapered, space-tapered, and hybrid-tapered designs. All designs achieve a sub-1° half-power beamwidth (HPBW), peak sidelobe levels (SLL) below -26 dB, and effective isotropic radiated power (EIRP) of 100 dBm at broadside with a ±60° grating-lobe-free scan range. Furthermore, a small-scale demonstrator is realized and presented that consists of an eight-layer printed circuit board (PCB) with 8× 8 patch antennas on one side and a 4× 4 open-ended waveguide (OEWG) feed-array with beamforming chips and integrated liquid cooling channels on the other side. After over-the-air calibration, the realized array achieves a peak EIRP of 56 dBm, a 12° HPBW, and the ability to scan to ±60° in the H-plane with cos θ scan loss and no grating lobes. E-plane scanning is achieved up to ±45° with θ 1.1 scan loss.

In this paper, we propose a novel method for point cloud complementation called PADPNet. Our approach uses a combination of global and local information to infer missing elements in the point cloud. We achieve this by dividing the input point cloud into uniform local regions, called perceptual fields, which are abstractly understood as special convolution kernels. The set of point clouds in each local region is represented as a feature vector and transformed into N uniform perceptual fields as the input to our transformer model. We also designed a geometric density-aware block to better exploit the inductive bias of the point cloud’s 3D geometric structure. Our method preserves sharp edges and detailed structures that are often lost in voxel-based or point-based approaches. Experimental results demonstrate that our approach outperforms other methods in reducing the ambiguity of output results. Our proposed method has important applications in 3D computer vision and can efficiently recover complete 3D object shapes from missing point clouds.

This study presents a novel multimodal heterogeneous perception cross-fusion framework for intelligent vehicles that combines data from millimeter-wave radar and camera to enhance target tracking accuracy and handle system uncertainties. The framework employs a multimodal interaction strategy to predict target motion more accurately and an improved joint probability data association method to match measurement data with targets. An adaptive root-mean-square cubature Kalman filter is used to estimate the statistical characteristics of noise under complex traffic scenarios with varying process and measurement noise. Experiments conducted on a real vehicle platform demonstrate that the proposed framework improves reliability and robustness in challenging environments. It overcomes the challenges of insufficient data fusion utilization, frequent leakage, and misjudgment of dangerous obstructions around vehicles, and inaccurate prediction of collision risks. The proposed framework has the potential to advance the state of the art in target tracking and perception for intelligent vehicles.

This deliverable describes the EU Demand Side Radar and its design and implementation activity. The online radar visualises use cases and demands scenarios based on CEI technologies, vertical sectors and readiness for implementation. Following the introduction, the document describes the steps taken to specify the design, collect the information and develop the EU Demand Side Radar. The core part of the document shows the latest development of the radar and the visualisation dashboard available on www.eucloudedgeiot.eu. In the last chapter of the document, the planning and the next steps of the EU demand side radar until the publication of the first official version are described.

Weather radar plays an important role in accurate weather monitoring and modern weather forecasting, as it can provide timely and refined weather forecasts for the public and for decision makers. Deep learning has been applied in radar nowcasting tasks and has exhibited a better performance than traditional radar echo extrapolation methods. However, current deep learning-based radar nowcasting models are found to suffer from a spatial “blurry” effect that can be attributed to a deficiency in spatial variability representation. This study proposes a Spatial Variability Representation Enhancement (SVRE) loss function and an effective nowcasting model, named the Attentional Generative Adversarial Network (AGAN), to alleviate this blurry effect by enhancing the spatial variability representation of radar nowcasting. An ablation experiment and a comparison experiment were implemented to assess the effect of the generative adversarial (GA) training strategy and the SVRE loss, as well as to compare the performance of the AGAN and SVRE loss function with the current advanced radar nowcasting models. The performances of the models were validated on the whole test set and inspected in two storm cases. The results showed that both the GA strategy and SVRE loss function could alleviate the blurry effect by enhancing the spatial variability representation, which helps the AGAN to achieve better nowcasting performance than the other competitor models. Our study provides a feasible solution for high-precision radar nowcasting applications.

Synthetic Aperture Radar (SAR) renderings in 3D provide additional target information when compared to 2D by separating out features overlaid in height. However, the required 2D SAR aperture, when Nyquist sampled, necessitates large scanning times that would be impractical for most realistic collections. This research has developed a novel volumetric approach to sparse aperture 3D SAR imaging, that is applicable to bistatic SAR near-field geometries, a generalization of far-field cases. This approach is first demonstrated in simulation and then applied to a measured scene containing a model vehicle target, producing sub-Nyquist sampled 3D SAR renderings.

Understanding the contribution of the Antarctic Ice Sheet (AIS) to past and future sea-level rise has emerged as a scientific priority over the last four decades. Whilst our knowledge of ice-dynamical changes occurring as a result of current anthropogenic forcing has improved considerably since the start of the satellite era, significantly less is known about the evolution of the AIS during the pre-industrial Holocene (the last ~11.7 thousand years; ka). Quantifying these changes is crucial, however, as this time period corresponds to a time when the ice sheet was retreating from its maximal extent at the Last Glacial Maximum (LGM; ~20 ka) and environmental conditions were similar to today. Therefore, improving our understanding of this period may provide a long-term context to the decadal changes observed in recent times and how these may evolve in the future. Whilst point-based geochronological measurements of ice and sediment cores, or surface exposure dating, can be used to assess past ice-sheet changes over the AIS, it remains unclear how representative they are of a wider region. A complementary and spatially extensive resource across the ice sheet are Internal Reflecting Horizons (IRHs) as imaged by Radio-Echo Sounding (RES) techniques, which provide a cumulative record of accumulation, basal melt and ice dynamics that, if dated precisely at ice cores, can be used to inform numerical ice-sheet models projecting past and future changes on large spatial scales. The aim of this thesis is therefore to develop and extend age-depth models from IRHs across the AIS to assess the past stability of the ice sheet. In this thesis, an age-depth model of Pine Island Glacier spanning the LGM and Holocene periods is derived from spatially extensive IRHs. The connection between RES profiles and the WAIS Divide ice core enables the direct dating of the IRHs, and reveals that they match large peaks in sulphate concentrations which are unparalleled in the 68,000 year-old record, thus suggesting that the cause of these IRHs is from past explosive volcanic eruptions. By connecting this IRH stratigraphy with a previously developed age-depth model across the Institute and Möller Ice Streams (IMIS), I show that a precisely dated age-depth model now exists over 20% of the West Antarctic Ice Sheet (WAIS). One of these IRHs, precisely dated at ~4.7 ka, is then used as input into a one-dimensional ice-flow model to estimate past accumulation rates during the mid-Holocene over the catchments encompassing Pine Island Glacier, Thwaites Glacier, and IMIS, together representing 30% of the WAIS. The inferred mid-Holocene accumulation estimates are then compared with modern rates derived from climate models and observational measurements to show that accumulation rates were 18% greater during the last five millennia compared to the present over the Amundsen-Weddell-Ross Divide. These results match previous findings from isolated ice-core measurements and spatially targeted studies over the divide, and correspond to periods of grounding line retreat and readvance during the Holocene over the WAIS. Together, these show the potential for extracting further IRH information from other sectors of the AIS in order to build an age-depth model of the ice sheet. However, the underlying RES data necessary for this work were, until recently, relatively inaccessible to the wider scientific community, thus restricting the extraction and interpretation of age-depth models across the AIS. This motivated the release of ~300,000 line-km of RES profiles acquired by the British Antarctic Survey between 2004 and 2020. In addition to standardising and releasing these data, this thesis shows that large sections of continuous englacial layering exist widely across both East and West Antarctica, suggesting that, together with previously developed age-depth models of both regions and nearby ice-core stratigraphies, these newly released RES datasets will be critical in our aim to build an ice-sheet wide age-depth model of Antarctica, as motivated by the AntArchitecture Initiative. Together, the findings from this thesis reveal the spatially extensive nature of IRHs across West and East Antarctica and demonstrate how these can be used to infer past ice-sheet changes. This thesis also highlights the need to extract further age-depth models, particularly across East Antarctica, in order to provide important boundary conditions such as past accumulation rates and ice-elevation change which can be used by numerical ice-sheet models to help improve predictions of past and future ice-sheet change and ensuing sea-level rise contributions.

International audience; Metaheuristic for Optimal Dynamic K-Coloring Application on Band Sharing for Automotive Radars .

The number of vehicles equipped with radars on the road has been increasing for years and is expected to reach 50% of cars by 2030. This rapid rise in radars will likely increase the risk of harmful interference, especially since radar specifications from standardization bodies (e.g., ETSI) provide requirements in terms of maximum transmit power but do no mandate specific radar waveform parameters nor channel access scheme policies. Techniques for interference mitigation are thus becoming very important to ensure the long-term correct operation of radars and upper-layer ADAS systems that depend on them in this complex environment. In our previous work, we have shown that organizing the radar band into time-frequency resources that do not interfere with each other vastly reduces the amount of interference by facilitating band sharing. In this paper, a metaheuristic is presented to find the optimal resource sharing between radars, knowing their relative positions and thereby the line-of-sight and non-line-of-sight interference risks during a realistic scenario. The metaheuristic aims at optimally minimizing interference while minimizing the number of resource changes that radars have to make. It is a centralized approach where everything about the system is known (e.g., the past and future positions of the vehicles). This and the high computational load induce that this algorithm is not meant to be used in real-time. However, the metaheuristic approach can be extremely useful for finding near optimal solutions in simulations, allowing for the extraction of efficient patterns, or as data generation for machine learning.

The change in raindrop spectrum characteristics is an important factor affecting the accuracy of estimations of precipitation. The in-depth study of raindrop spectrum characteristics is of great interest for understanding precipitation process and improving quantitative radar precipitation estimation. In this paper, the raindrop size distributions at Longli (57913), Puding (57808) and Luodian (57916) stations in Guizhou were analyzed from the perspective of precipitation microphysical characteristics. The results showed that the raindrop size distribution was different among different regions. The correlation coefficients of the mass-weighted average diameter for the rain intensities at these three stations were 46.89%, 49.51%, and 47.03%, respectively, which were slightly lower than the normal correlation coefficients of the average volume diameter for the rain intensities: 67.80%, 71.28%, and 71.46%, respectively. Based on the data from the Guiyang weather radar, raindrop spectrometer, and automatic rain gauge, the dynamic Z-I relationship method and the LSTM neural network method were used to estimate precipitation. The correlation coefficients of the dynamic Z-I relationship method and the LSTM neural network method at the three stations studied were 0.8432, 0.7763, and 0.8658 and 0.8745, 0.9125, and 0.8676, respectively. Regarding the process of stratiform cloud precipitation, the correlation coefficients of the dynamic Z-I relationship method and LSTM neural network method at the three stations were 0.6933, 0.0902, and 0.1409 and 0.7114, 0.4984, and 0.4902, respectively. In the estimation of cumulative precipitation for 45 days from 1 July to 16 August 2020, the relative errors of the neural network estimation at the three stations were −4.25%, −11.35%, and −8.68% and the relative errors of the dynamic Z-I relationship estimation were −2.68%, −7.41%, and −21.23%, respectively. The final relative error of the neural network was slightly worse than that of the dynamic Z-I relationship in the cumulative precipitation estimations of Longli station and Puding station, but the overall correlation coefficients of the LSTM neural network method were better than those of the dynamic Z-I relationship method.

Inundation dynamics are the primary control on greenhouse gas emissions from peatlands. Situated in the central Congo Basin, the Cuvette Centrale is the largest tropical peatland complex. However, our knowledge of the spatial and temporal variations in its water levels is limited. By addressing this gap, we can quantify the relationship between the Cuvette Centrale’s water levels and greenhouse gas emissions, and further provide a baseline from which deviations caused by climate or land-use change can be observed, and their impacts understood. We present here a novel approach that combines satellite-derived rainfall, evapotranspiration and L-band Synthetic Aperture Radar (SAR) data to estimate spatial and temporal changes in water level across a sub-region of the Cuvette Centrale. Our key outputs are a map showing the spatial distribution of rainfed and flood-prone locations and a daily, 100 m resolution map of peatland water levels. This map is validated using satellite altimetry data and in situ water table data from water loggers. We determine that 50% of peatlands within our study area are largely rainfed, and a further 22.5% are somewhat rainfed, receiving hydrological input mostly from rainfall (directly and via surface/sub-surface inputs in sloped areas). The remaining 27.5% of peatlands are mainly situated in riverine floodplain areas to the east of the Congo River and between the Ubangui and Congo rivers. The mean amplitude of the water level across our study area and over a 20-month period is 22.8 ± 10.1 cm to 1 standard deviation. Maximum temporal variations in water levels occur in the riverine floodplain areas and in the inter-fluvial region between the Ubangui and Congo rivers. Our results show that spatial and temporal changes in water levels can be successfully mapped over tropical peatlands using the pattern of net water input (rainfall minus evapotranspiration, not accounting for run-off) and L-band SAR data.

Inundation dynamics are the primary control on greenhouse gas emissions from peatlands. Situated in the central Congo Basin, the Cuvette Centrale is the largest tropical peatland complex. However, our knowledge of the spatial and temporal variations in its water levels is limited. By addressing this gap, we can quantify the relationship between the Cuvette Centrale’s water levels and greenhouse gas emissions, and further provide a baseline from which deviations caused by climate or land-use change can be observed, and their impacts understood. We present here a novel approach that combines satellite-derived rainfall, evapotranspiration and L-band Synthetic Aperture Radar (SAR) data to estimate spatial and temporal changes in water level across a sub-region of the Cuvette Centrale. Our key outputs are a map showing the spatial distribution of rainfed and flood-prone locations and a daily, 100 m resolution map of peatland water levels. This map is validated using satellite altimetry data and in situ water table data from water loggers. We determine that 50% of peatlands within our study area are largely rainfed, and a further 22.5% are somewhat rainfed, receiving hydrological input mostly from rainfall (directly and via surface/sub-surface inputs in sloped areas). The remaining 27.5% of peatlands are mainly situated in riverine floodplain areas to the east of the Congo River and between the Ubangui and Congo rivers. The mean amplitude of the water level across our study area and over a 20-month period is 22.8 ± 10.1 cm to 1 standard deviation. Maximum temporal variations in water levels occur in the riverine floodplain areas and in the inter-fluvial region between the Ubangui and Congo rivers. Our results show that spatial and temporal changes in water levels can be successfully mapped over tropical peatlands using the pattern of net water input (rainfall minus evapotranspiration, not accounting for run-off) and L-band SAR data.

The objective of this work is to propose the development of a virtual electronics laboratory with an aeronautical radar simulator using immersive technologies to help students learn. To verify whether this proposal was viable, the systematic literature review (SLR) methodology was used, whose objective was to verify whether immersive technologies were being used effectively in education and, also, what challenges, opportunities, and benefits they bring to Education 4.0. For this, eight Research Questions (RQs) were formulated to be answered by articles based on the highest SLR scores. The results presented by SLR were as follows: there was an increase in the use of immersive technologies in education, but virtual reality (VR) is still more used in education than AR, despite VR being more expensive than AR; the use of these new technologies brings new challenges, opportunities, and benefits for education; there was an increase in the quality of teaching for complex subjects; and there was an increase in students’ interest in the content presented.

Detailed within is an attempt to implement a real-time radar signal classification system to monitor and count bee activity at the hive entry. There is interest in keeping records of the productivity of honeybees. Activity at the entrance can be a good measure of overall health and capacity, and a radar-based approach could be cheap, low power, and versatile, beyond other techniques. Fully automated systems would enable simultaneous, large-scale capturing of bee activity patterns from multiple hives, providing vital data for ecological research and business practice improvement. Data from a Doppler radar were gathered from managed beehives on a farm. Recordings were split into 0.4 s windows, and Log Area Ratios (LARs) were computed from the data. Support vector machine models were trained to recognize flight behavior from the LARs, using visual confirmation recorded by a camera. Spectrogram deep learning was also investigated using the same data. Once complete, this process would allow for removing the camera and accurately counting the events by radar-based machine learning alone. Challenging signals from more complex bee flights hindered progress. System accuracy of 70% was achieved, but clutter impacted the overall results requiring intelligent filtering to remove environmental effects from the data.

This article introduces a simple, but novel, concept of a single-layer dual-frequency microstrip patch antenna array for joint communication and sensing operating at 24 and 28 GHz. The frequencies are separated by polarization: the radar sensing function at the 24-24.25 GHz ISM band is vertically polarized, whilst communication at the n261 (27.5-28.35 GHz) band for 5G NR uses horizontal polarization. Based on simulation results, the antenna array is expected to achieve an azimuthal scan range of ±45◦ at the radar band with frequency scanning in elevation limited to only ±1◦. The scan ranges for communication are ±45◦ and ±40◦ in azimuth and elevation, respectively.

Assimilating the proper amount of water vapor into a numerical weather prediction (NWP) model is essential in accurately forecasting a heavy rainfall. Radar data assimilation can effectively initialize the three-dimensional structure, intensity, and movement of precipitation fields to an NWP at a high resolution (±250 m). However, the in-cloud water vapor amount estimated from radar reflectivity is empirical and assumes that the air is saturated when the reflectivity exceeds a certain threshold. Previous studies show that this assumption tends to overpredict the rainfall intensity in the early hours of the prediction. The purpose of this study is to reduce the initial value error associated with the amount of water vapor in radar reflectivity by introducing advanced remote sensing data. The ongoing research shows that errors can be largely solved by assimilating satellite all-sky radiances and global positioning system radio occultation (GPSRO) refractivity to enhance the moisture analysis during the cycling period. The impacts of assimilating moisture variables from satellite all-sky radiances and GPSRO refractivity in addition to hydrometeor variables from radar reflectivity generate proper amounts of moisture and hydrometeors at all levels of the initial state. Additionally, the assimilation of satellite atmospheric motion vectors (AMVs) improves wind information and the atmospheric dynamics driving the moisture field which, in turn, increase the accuracy of the moisture convergence and fluxes at the core of the convection. As a result, the accuracy of the timing and intensity of a heavy rainfall prediction is improved, and the hourly and accumulated forecast errors are reduced.

In this article, we propose a framework for contactless human-computer interaction (HCI) using novel tracking techniques based on deep learning-based super-resolution and tracking algorithms. Our system offers unprecedented high-resolution tracking of hand position and motion characteristics by leveraging spatial and temporal features embedded in the reflected radar waveform. Rather than classifying samples from a predefined set of hand gestures, as common in existing work on deep learning with mmWave radar, our proposed imager employs a regressive full convolutional neural network (FCNN) approach to improve localization accuracy by spatial super-resolution. While the proposed techniques are suitable for a host of tracking applications, this article focuses on their application as a musical interface to demonstrate the robustness of the gesture sensing pipeline and deep learning signal processing chain. The user can control the instrument by varying the position and velocity of their hand above the vertically-facing sensor. By employing a commercially available multiple-input-multiple-output (MIMO) radar rather than a traditional optical sensor, our framework demonstrates the efficacy of the mmWave sensing modality for fine motion tracking and offers an elegant solution to a host of HCI tasks. Additionally, we provide a freely available software package and user interface for controlling the device, streaming the data to MATLAB in real-time, and increasing accessibility to the signal processing and device interface functionality utilized in this article., Accepted to IEEE Transactions on Multimedia

This paper presents a 160 GHz, D-band, low-noise amplifier (LNA) and a D-band power amplifier (PA) implemented in the Global Foundries 22 nm CMOS FDSOI. The two designs are used for the contactless monitoring of vital signs in the D-band. The LNA is based on multiple stages of a cascode amplifier topology with a common source topology adopted as the input and output stages. The input stage of the LNA is designed for simultaneous input and output matching, while the inter-stage-matching networks are designed for maximizing the voltage swing. The LNA achieved a maximum gain of 17 dB at 163 GHz. The input return loss was quite poor in the 157–166 GHz frequency band. The −3 dB gain bandwidth corresponded to 157–166 GHz. The measured noise figure was between 7.6 dB and 8 dB within the −3 dB gain bandwidth. The power amplifier achieved an output 1 dB compression point of 6.8 dBm at 159.75 GHz. The measured power consumptions of the LNA and the PA were 28.8 mW and 10.8 mW, respectively.

Vehicle safety promises to be one of the Advanced Driver Assistance System’s (ADAS) biggest benefits. Higher levels of automation remove the human driver from the chain of events that can lead to a crash. Sensors play an influential role in vehicle driving as well as in ADAS by helping the driver to watch the vehicle’s surroundings for safe driving. Thus, the driving load is drastically reduced from steering as well as accelerating and braking for long-term driving. The baseline for the development of future intelligent vehicles relies even more on the fusion of data from surrounding sensors such as Camera, LiDAR and Radar. These sensors not only need to perceive in clear weather but also need to detect accurately adverse weather and illumination conditions. Otherwise, a small error could have an incalculable impact on ADAS. Most of the current studies are based on indoor or static testing. In order to solve this problem, this paper designs a series of dynamic test cases with the help of outdoor rain and intelligent lightning simulation facilities to make the sensor application scenarios more realistic. As a result, the effect of rainfall and illumination on sensor perception performance is investigated. As speculated, the performance of all automotive sensors is degraded by adverse environmental factors, but their behaviour is not identical. Future work on sensor model development and sensor information fusion should therefore take this into account.

With the continuous use of various new radar systems and complex radar systems, the electromagnetic environment is extremely deteriorated. The traditional emitter recognition methods have been difficult to meet the requirements of recognition performance in the rapidly changing battlefield environment. To solve this problem, a radar electronic signal recognition algorithm based on wavelet transform and deep learning is proposed in this paper. Starting from the radar reconnaissance system, the causes of signal preprocessing are analyzed, and the methods of signal denoising, signal normalization, signal intra pulse modulation recognition, multipath signal detection and suppression are deeply studied. In particular, the denoising algorithm based on threshold wavelet transform is proposed, which significantly improves the reliability of the algorithm. Aiming at the individual feature extraction of emitter signal, the extraction methods of emitter signal time domain feature, frequency domain feature, fuzzy function slice feature and cyclic spectrum feature based on wavelet transform are studied and analyzed, which provides stable and reliable classification features for emitter signal recognition. According to the characteristics of radar emitter signal, an optimized convolution neural network is designed, and the feature fusion processing is carried out at the decision-making level, which greatly improves the recognition effect and enhances the robustness of the recognition system. Experiments on radar data show that the fusion recognition rate is higher than any single feature and has strong robustness. In addition, compared with the traditional SVM and elm networks, the CNN network proposed in this paper can extract detailed features more effectively and improve the recognition rate of electronic radar.

In this paper, a method of enhancing gain in a microstrip patch array antenna using two metallic plates for 24 GHz radar applications is presented. A 4 × 1 linear microstrip square patch array antenna covering the 24.0 to 24.25 GHz frequency range and using a shunt-connected series feed network with a tapered power distribution was first designed with a measured maximum gain of 9.8 dBi and dimensions of 30 mm × 12 mm. Two metallic plates were appended along the array axis of the antenna to double the gain in the 4 × 1 array antenna. Effects on performance from varying the tilting angle and length of the metallic plates, such as the input reflection coefficient, the radiation patterns, and gain, were investigated through simulation. Gain enhancement in the 4 × 1 patch array antenna with metallic plates was highest when the tilting angle was around 70∘, and gain kept increasing as the length of the metallic plates increased. A prototype of the 4 × 1 patch array antenna was fabricated with plates at a tilting angle of 70∘, a length of 50 mm, and a measured maximum gain of 16.8 dBi. Therefore, a gain enhancement of about 7 dB was achieved by adding the metallic plates along the array axis of the 4 × 1 patch array antenna.

This thesis develops the technique of millimetre-wave radar at 94 GHz for close-range remote sensing of glaciers and terrestrial snow cover (the cryosphere). The capabilities of 94 GHz radar for cryosphere mapping are demonstrated using the 2ⁿᵈ generation All-weather Volcano Topography Imaging Sensor (AVTIS2), which maps 3D terrain from real-beam scanning. AVTIS2 acquires 3D point clouds of terrain and a comparison to co-located high density point clouds derived from Terrestrial Laser Scanner (TLS) data showed that AVTIS2 point cloud uncertainties were 1.5 m at 1.5 km and 3 m at 3 km. These values are smaller than other close-range radar systems used to map cryospheric terrain in 3D. Next, the distribution of Normalised Radar Cross Section (σ⁰) values over glacier ice at 94 GHz was found to be −17.0 < σ⁰ < −3.4; σ⁰ₘₑₐₙ = −9.9 dB and followed a log-normal distribution. These values are comparable to other terrain types at 94 GHz such as refrozen snow, wet snow and wet soil, hence glacier surfaces were found to be suitable targets for terrain mapping at 94 GHz. These fundamental results were used to apply the AVTIS2 94 GHz radar for monitoring snow cover and glacier calving. Field trials in Scotland showed that the transition of wet snow to a refrozen snowpack in response to reductions in air temperature could be detected from changes in 94 GHz σ⁰. Snow hazard features could also be identified, such as post-avalanche debris accumulations which manifest as localised increases in radar backscatter. Next, 3D AVTIS2 data was used to quantify glacier calving rates at the Hansbreen tidewater glacier in Svalbard. A time series of ice cliff morphology derived from the AVTIS2 3D data sets exposed the role of melt undercutting and demonstrated the critical role of the ocean on calving processes in Svalbard. The high accuracy 3D data acquired from AVTIS2 and the sensitivity of radar backscatter to surface changes has demonstrated the unique capabilities of 94 GHz radar for cryosphere mapping, paving the way forward for new applications and opportunities for close-range remote sensing of the cryosphere.

Airborne scatterometer capability depends on not only the device’s technical characteristics but also the scheme used for surface observations. Typically, a rotating-beam scatterometer uses a circular scheme for sampling normalized radar cross-sections (NRCS) at wind measurements over the sea. Here, we investigate wind retrieval using an updated semicircular scheme, providing the NRCS sampling at various combinations of incidence angles within the range 30° to 60°. The effectiveness of the wind retrieval using our semicircular sampling scheme was evaluated using Monte Carlo simulations, and we then developed corresponding wind algorithms that used a geophysical model function (GMF). As a result of the study, we found that a semicircular sampling scheme is well suited for wind retrieval over the sea using a rotating-beam scatterometer. We showed that a semicircular scheme can provide wind retrieval accuracies similar to those achievable with a conventional circular scheme, although the semicircular scheme requires approximately three times the number of NRCS samples integrated in each azimuth sector. Most importantly, however, the semicircular scheme enabled a maximum altitude for wind retrieval of twice the height possible with a circular scheme. In this study, we also demonstrate that the wind speed accuracy tends to increase with an increase in the incidence angle and similarly for the wind direction accuracy. Nonetheless, we then show that the simultaneous use of the NRCS sampling scheme at several incidence angles can increase the wind retrieval accuracy, especially when three or four incidence angles are used. The obtained results can be used to enhance airborne scatterometers and multimode radars operated in a scatterometer mode, including airborne high-altitude conical scanning radars, and can be applied to new remote sensing systems’ development.

Die Radartechnologie ist für viele zivile Anwendungen von entscheidender Bedeutung geworden, doch viele alte Probleme stellen nach wie vor eine Herausforderung dar. Um diese Herausforderungen zu bewältigen, werden innovative und robuste Signalverarbeitungstechniken benötigt. In dieser Arbeit werden neue Lösungen aus Sicht der Informationstheorie und des maschinellen Lernens vorgeschlagen. Im ersten Hauptteil der Arbeit schlagen wir vor, die potenziellen Auswirkungen der unerwünschten Umgebungseinflüsse durch die Gestaltung der Signalform zu überwinden. Dazu schlagen wir verschiedene Strategien vor, die auf der Betriebsart des Radars und den verfügbaren Vorabinformationen basieren. Im zweiten Hauptteil gehen wir mit datengesteuerten Ansätzen auf allgemeine Probleme der DOA-Algorithmen ein, wobei wir beweisen, dass neuronale Netze die eingehenden Signale entrauschen und die schlechte Auflösung kleiner Arrays überwinden können., Radar technology has become key for many civilian applications but a lot of inherited problems remain a challenge. To address these challenges, innovative and robust signal processing techniques are needed. This thesis proposes novel solutions from information theory and machine learning perspectives. In the first main part of the thesis, we propose to overcome the potential effects of the environmental undesired returns through waveform design. Thus, we propose different strategies to tackle this problem based on the radar operation mode and the priori information available. In the second main part, armed with data driven approaches, we address DOA algorithms common problems, where we prove that neural networks can denoise the incoming signals and can overcome the poor resolution of small arrays.

This paper presents a method for estimating the position of a target under jammed conditions using the Time Difference of Arrival (TDOA) method. The algorithm utilizes a deep neural network to overcome the challenges posed by the jammed conditions. The simulations and results indicate that the presented method is more accurate and efficient than the traditional TDOA methods.

Path planning and localization in low-light and inclement weather conditions are critical problems facing autonomous vehicle systems. Our proposed method applies a single modality, millimetre-wave radar perception system for the detection of roadside retro-reflectors. Radar-based perception tasks can be challenging to perform due to the sparse and noisy nature of radar data. We propose the use of an unsupervised learning approach for filtering radar point clouds through Density-Based Spatial Clustering of Applica- tions with Noise (DBSCAN). The DBSCAN algorithm segments retro-reflector points from noise points, thus providing the autonomous vehicle with a predicted path for the road ahead. We tested the approach via indoor experiments that make use of Continental’s ARS 408 radar, a mobile Husky A2000 robot, and a Vicon motion capture system for ground truth validation. The experimental results of the proposed system demonstrated a classification accuracy of 84.13 % and F1 score of 83.71 %.

In this paper, we address the problem of classifying data within the radar reference window in terms of statistical properties. Specifically, we partition these data into statistically homogeneous subsets by identifying possible clutter power variations with respect to the cells under test (accounting for possible range-spread targets) and/or clutter edges. To this end, we consider different situations of practical interest and formulate the classification problem as multiple hypothesis tests comprising several models for the operating scenario. Then, we solve the hypothesis testing problems by resorting to suitable approximations of the model order selection rules due to the intractable mathematics associated with the maximum likelihood estimation of some parameters. Remarkably, the classification results provided by the proposed architectures represent an advanced clutter map since, besides the estimation of the clutter parameters, they contain a clustering of the range bins in terms of homogeneous subsets. In fact, such information can drive the conventional detectors towards more reliable estimates of the clutter covariance matrix according to the position of the cells under test. The performance analysis confirms that the conceived architectures represent a viable means to recognize the scenario wherein the radar is operating at least for the considered simulation parameters., Accepted by IEEE Transactions on Aerospace and Electronic Systems

Deformations affect the structural integrity of wind turbine towers. The health of such structures is thus assessed by monitoring. The majority of sensors used for this purpose are costly and require in situ installations. We investigated whether Multiple-Input Multiple-Output Synthetic Aperture Radar (MIMO-SAR) sensors can be used to monitor wind turbine towers. We used an automotive-grade, low-cost, off-the-shelf MIMO-SAR sensor operating in the W-band with an acquisition frequency of 100 (Formula presented.) to derive Line-Of-Sight (LOS) deformation measurements in ranges up to about 175 (Formula presented.). Time series of displacement measurements for areas at different heights of the tower were analyzed and compared to reference measurements acquired by processing video camera recordings and total station measurements. The results showed movements in the range of up to 1 (Formula presented.) at the top of the tower. We were able to detect the deformations also with the W-band MIMO-SAR sensor; for areas with sufficient radar backscattering, the results suggest a sub-mm noise level of the radar measurements and agreement with the reference measurements at the mm- to sub-mm level. We further applied Fourier transformation to detect the dominant vibration frequencies and identified values ranging from 0.17 to 24 (Formula presented.). The outcomes confirmed the potential of MIMO-SAR sensors for highly precise, cost-efficient, and time-efficient structural monitoring of wind turbine towers. The sensors are likely also applicable for monitoring other high-rise structures such as skyscrapers or chimneys., Energies, 16 (3), ISSN:1996-1073

Contactless vital sign measurement technologies have the potential to greatly improve patient experiences and practitioner safety while creating the opportunity for comfortable continuous monitoring. We introduce a contactless alternative for measuring human heart sounds. We leverage millimeter wave frequency-modulated continuous wave radar and multi-input multi-output beamforming techniques to capture fine skin vibrations that result from the cardiac movements that cause heart sounds. We discuss contact-based heart sound measurement techniques and directly compare the radar heart sound technique with these contact-based approaches. We present experimental cases to test the strengths and limitations of both the contact-based measurement techniques and the contactless radar measurement. We demonstrate that the radar measurement technique is a viable and potentially superior method for capturing human heart sounds in many practical settings.

Vital signs estimation provides valuable information about an individual’s overall health status. Gathering such information usually requires wearable devices or privacy-invasive settings. In this work, we propose a radar-based user-adaptable solution for respiratory signal prediction while sitting at an office desk. Such an approach leads to a contact-free, privacy-friendly, and easily adaptable system with little reference training data. Data from 24 subjects are preprocessed to extract respiration information using a 60 GHz frequency-modulated continuous wave radar. With few training examples, episodic optimization-based learning allows for generalization to new individuals. Episodically, a convolutional variational autoencoder learns how to map the processed radar data to a reference signal, generating a constrained latent space to the central respiration frequency. Moreover, autocorrelation over recorded radar data time assesses the information corruption due to subject motions. The model learning procedure and breathing prediction are adjusted by exploiting the motion corruption level. Thanks to the episodic acquired knowledge, the model requires an adaptation time of less than one and two seconds for one to five training examples, respectively. The suggested approach represents a novel, quickly adaptable, non-contact alternative for office settings with little user motion., ITEA3 Unleash Potentials in Simulation (UPSIM) project (N°19006) German Federal Ministry of Education and Research (BMBF), Austrian Research Promotion Agency (FFG), Rijksdienst voor Ondernemend Nederland (Rvo), Innovation Fund Denmark (IFD)

Electrical impedance tomography (EIT) is an imaging modality that can estimate a visualization of the conductivity distribution inside the human body. However, the spatial resolution of EIT is limited because measurements are sensitive to noise. We investigate a technique to incorporate a priori information into the EIT reconstructions of the D-Bar algorithm. Our paper aims to help engineers understand the behavior of the D-Bar algorithm and its implementation. The a priori information is provided by a radar setup and a one-dimensional reconstruction of the radar data. The EIT reconstruction is carried out with a D-Bar algorithm. An intermediate step in the D-Bar algorithm is the scattering transform. The a priori information is added in this exact step to increase the spatial resolution of the reconstruction. As the D-Bar algorithm is widely used in the mathematical community and thus far has limited usage in the engineering domain, we also aim to explain the implementation of the algorithm and give an intuitive understanding where possible. Different parameters of the reconstruction algorithm are analyzed systematically with the help of the GREIT figures of merit. Even a limited one-dimensional a priori information can increase the reconstruction quality considerably. Artifacts from noisy EIT measurements are reduced. However, the selection of the amount of a priori information and the estimation of its value can worsen the reconstruction results again.

Given that the recent rapid growth of offshore production, especially in the Arctic region of the Russian Federation, is causing increased concern about oil spills on the water surface, this issue is especially relevant and important today. These pollutants have a devastating impact on the world’s marine biosphere. Therefore, effective and reliable methods and instruments must be used for operational spill detection in order to detect a remote oil spill. Several methods for oil spill monitoring and Russian developments in this area were described, including their features, advantages, and drawbacks. In cases when use in difficult Arctic conditions was anticipated, due to the harsh climate and ice-covered water surface, it was not always possible for spill detection instruments to be utilized. Despite this, such methods as radar, infrared, and ultraviolet were proven to be effective during this research. Ultimately, the combination of these methods returned the greatest volume of information to offshore platform staff about a detected oil spill. The information provided includes the spread area of the spill, the thickness of the leak, and the chemical composition of the oil.

The accuracy and real-time performance of the speed measurement method were important factors influencing the accuracy of the seeding spacing monitoring. In this study, three different speed measurement methods (including GNSS (Global Navigation Satellite System) receiver speed measurement, radar speed measurement, and encoder measurement rotation speed) were used to compare and analyze the monitoring results of seeding spacing. The same monitoring system was used to calculate the seeding spacing under three different speed measurement methods. The encoder directly measured the rotation speed of the seeding disc instead of the traditional method of measuring the rotation speed of the driving wheel. The monitoring results of uniform-speed and variable-speed seeding field tests showed that the three speed measurement methods have extremely high correlations, with a correlation coefficient R > 0.95. There was small difference among the three speed measurement methods, and they all met the needs of field seeding operations. After comprehensively considering the use cost and installation complexity, it was recommended to use GNSS receiver speed measurement to monitor the speed of the seeding operation and the real-time seeding spacing.

У цій статті описано просту методику отримання надширокосмугової характеристики з подвійною поляризацією і підвищеними характеристиками випромінювання мініатюрної компактної антенної решітки для застосування в багатоцільових РЛС С-діапазону. Запропонована антена складається з головного Т-перехідного дільника потужності і 32 круглих випромінюючих елементів. Вона виготовлена на епоксидній смолі FR 4 товщиною 1,58 мм з мініатюрними розмірами (110 мм x 214 мм x 1,58 мм). Метою роботи є дослідження надширокосмугової характеристики та підвищеної ефективності діаграми спрямованості, що складається з високої поляризації на резонансних частотах і низької перехресної поляризації для мініатюрної структури антени, яка покриває весь С-діапазон. Запланована конструкція оптимальної структури була отримана після опрацювання багатьох етапів, починаючи від базової антени та її оптимізації до остаточної запропонованої структури антенної решітки. Всі імітаційні дослідження були проведені з використанням чисельних методів і підтверджені розрахунками. Запропонована решітчаста антена добре мініатюризована і має гарні характеристики з точки зору смуги пропускання (122 %), коефіцієнта підсилення (15-24 дБ), рівня бічних пелюсток і перехресних поляризацій, які є необхідними в більшості областей обробки радіолокаційних сигналів. This paper describes a simple methodology to obtain an Ultra-Wide Band characteristic with dual polarization and high radiation performance of a miniaturized compact array antenna for Multi-Mission C band Radars applications. The proposed array antenna is composed of a principal T-junction power divider and 32 circular radiating elements. It is manufactured on 1.58 mm thick FR 4 epoxy with miniaturized dimensions (110 mm x 214 mm x 1.58 mm). Design objective adopted in this work is to explore the UltraWide Band characteristic and high radiation pattern performance consisting of high co-polarization at resonance frequencies and low cross-polarization for a miniaturized antenna structure that covers the entire C band. The intended design of the optimal proposed structure has been obtained after processing through many steps, starting from the basic antenna and its optimization to the final proposed array antenna structure. All the simulation investigations have been carried out using numerical methods and verified with measurement. The suggested array antenna is well miniaturized and it has good characteristics in terms of bandwidth (122 %), gain (15-24 dB), side lobes level and co-cross polarizations which are required in the majority of radars processing area.

This paper proposes a method for efficient and accurate removal of grating lobes in automotive Synthetic Aperture Radar (SAR) images. Grating lobes can indeed be mistaken as real targets, inducing in this way false alarms in the target detection procedure. Grating lobes are present whenever SAR focusing is performed using data acquired on a non-continuous basis. This kind of acquisition is typical in the automotive scenario, where regulations do not allow for a continuous operation of the radar. Radar pulses are thus transmitted and received in bursts, leading to a spectrum of the signal containing gaps. We start by deriving a suitable reference frame in which SAR images are focused. It will be shown that working in this coordinate system is particularly convenient since it allows for a signal spectrum that is space-invariant and with spectral gaps described by a simple one-dimensional function. After an inter-burst calibration step, we exploit these spectral characteristics of the signal by implementing a compressive sensing algorithm aimed at removing grating lobes. The proposed approach is validated using real data acquired by an eight-channel automotive radar operating in burst mode at 77 GHz. Results demonstrate the practical possibility to process a synthetic aperture length as long as up to 2 m reaching in this way extremely fine angular resolutions.

Airborne ground-penetrating synthetic aperture radar (GPSAR) using unmanned aerial vehicles (UAVs) offers advantages for many applications compared to established methods. In particular, these systems are useful for non-traversable terrain, where they could be used for the detection and localization of buried objects and structures. At distances of a few meters to the ground, refraction effects at the surface need to be considered in processing even for shallowly buried objects. Often the ground is assumed to be horizontal, which is not the case in many situations, such as uneven meadows or acres. This work investigates the influence of uneven ground on GPSAR images. It is shown that neglecting tilt in backprojection for shallowly buried targets leads to an apparent target shift. A model is derived to predict the shift for circular measurement trajectories and to estimate, if quality loss is to be expected. The model is verified using simulations, which are verified by measurement. It is concluded that tilt can be neglected in processing for shallowly buried targets. This is confirmed in a realistic measurement scenario., acceptedVersion

The ability to simulate radar responses from ground scenes in a tractable manner is the primary motivation of this thesis. Low cost simulation allows for better signal design, reconstruction algorithms and training data. This is possible within a ray-tracing framework. However, wave effects relevant to radar are neglected. Lifting the system to phase-space and considering coherence allows for a rigorous description of wave phenomena. Within this thesis, approaches to simplify and enable phase-space ray tracing for radar are developed. The phenomena of temporal coherence and its effect on interference and multipath is significant to radar phenomenology. The intensity of the signal captured from a ray-traced scene depends on the coherent sum of all rays and is expensive to compute. An approach to account for this interference is proposed, and implemented in addition to a phase-space rendering algorithm suitable for high-frequency signals. An efficiency and accuracy study is performed to validate the method. An antenna's energy distribution in position and spatial frequency is described by the Fourier transform of its mutual intensity function, and enables ray-tracing with diffraction. Analytical forms of the 4D Wigner function are found for polygonal planar sources via geometric transforms and Stokes' theorem. The sample space is tightened by identifying the Minkowski auto-mean as the spatial support of the energy distribution. This representation is shown to produce accurate radiation patterns when compared to traditional diffraction models. Uniform illumination of radiating sources is an idealised property which is rarely seen in reality. To enable simulation with accurate diffraction models, an experimental procedure for mapping antenna patterns and tomographic reconstruction in phase space is outlined and executed. This setup is also used as to evaluate the previously proposed approaches, and to provide insight for future directions.

The potential to improve safety on the roads by means of automotive radar systems has been investigated by vehicle manufacturers for many decades. In recent years, NCAP, the new car assessment program of of the European Ministry of Transport, has spurred further integration of 77 GHz automotive radars. The aim is to reduce the number of fatalities caused by human errors. SiGe-based radar MMICs have become established as a key technology, which can be implemented inexpensively with a high degree of digital circuit integration. In order to further reduce the fatalities, vulnerable road users must be identified by future radar systems. This can only be achieved by realizing a large number of radar channels. Currently, the bistatic approach (separate antennas for transmit and receive path) is mostly applied due to the lower complexity. With an increased number of radar channels, the implementation of the RF interfaces is becoming expensive. A monostatic approach with one antenna for the send and receive path would relax this issue. Passive duplexers, of which can be found in some current radar MMICs, show several disadvantages. Therefore, the focus of this work was placed on the development of a radar system that pursues an active duplexer solution. In particular, a novel design of an active quasi-circulator (QC) is proposed. For the implementation of the QC-based radar MMIC, Infineon provided two different SiGe processes. The developments were started with the pure bipolar-based B7HF200 process, which is the base for over 10 million radar MMICs on the road. As a further development, the enhanced B11HFC process was available, offering improved transit frequency and CMOS integration. In addition to the basic performance, care was taken in the QC design to find a robust solution against process variations. This is one of the fundamental aspects for MMICs in the high-volume segment in order to achieve a yield high enough to be economically feasible. The well-known problem of self-interference in monostatic radars due to antenna mismatch could also be solved by means of an innovative leakage canceler without degrading the remaining circuit performance. A construction of a custom on-wafer RF measurement setup was necessary to reflect the real environment of a radar system. This enables the identification of weaknesses in the respective components at an early stage of development. Further analyses illustrate that especially AM noise from signal sources in so-called blocker measurements prove to be critical. Employing a special AM noise measurement procedure, it can be shown that the noise performance of the DUT can still be measured despite the influence of the setup. It was thus possible to verify that the original noise values of the radar system are retained even in presence of antenna mismatch. The usability of the proposed radar system was demonstrated by FMCW outdoor measurements. The usage of a parabolic reflector based antenna with high gain, which also shows relatively high mismatch, enables testing of the integrated leakage canceler in a real environment. Despite these difficult external circumstances, no degradation of the radar’s dynamic could be observed. Consequently, a 7 cm tetrahedral reflector could be clearly detected at a distance of 100 m. submitted by Matthias Porranzl Dissertation Universität Linz 2023

This paper presents an accurate, highly efficient, and learning-free method for large-scale odometry estimation using spinning radar, empirically found to generalize well across very diverse environments -- outdoors, from urban to woodland, and indoors in warehouses and mines - without changing parameters. Our method integrates motion compensation within a sweep with one-to-many scan registration that minimizes distances between nearby oriented surface points and mitigates outliers with a robust loss function. Extending our previous approach CFEAR, we present an in-depth investigation on a wider range of data sets, quantifying the importance of filtering, resolution, registration cost and loss functions, keyframe history, and motion compensation. We present a new solving strategy and configuration that overcomes previous issues with sparsity and bias, and improves our state-of-the-art by 38%, thus, surprisingly, outperforming radar SLAM and approaching lidar SLAM. The most accurate configuration achieves 1.09% error at 5Hz on the Oxford benchmark, and the fastest achieves 1.79% error at 160Hz., Comment: Published in Transactions on Robotics. Edited 2022-11-07: Updated affiliation and citation

The vital sign measurement systems inside vehicles have taken great or impulse in recent years, and the technologies that have been most welcomed are based on wireless measurements that do not interrupt driving and are imperceptible to the driver. There are disorders in breathing patterns, which include the movement of different regions of the human thorax mainly. In this sense, the objective of this paper is to determine the dimensions of a lens-based antenna system and the pointing angles of the radiation main lobe of the six antenna-elements to measure the elongation of six regions of the human torso and determine breathing patterns. The pointing angle is controlled by the position of the antenna behind the lens, and the beamwidth of the lobe depends on the directivity achieved by the antenna. An electronic system to simulate torso elongation is also presented to calibrate and test the sensitivity of the system. This work was supported by the Spanish “Comisión Interministerial de Ciencia y Tecnologia” (CICYT) under projects PID2019-107885GB-C31 and MDM2016-O6OO; Catalan Research Group 2017 SGR 219; and ”Secretaría Nacional de Educación Superior, Ciencia, Tecnología e Innovación” (SENESCYT) from the Ecuadorian government.

Entlang der Signalverarbeitungskette von Radar Detektionen bis zur Fahrzeugansteuerung, diskutiert diese Arbeit eine semantischen Radar Segmentierung, einen darauf aufbauenden Radar SLAM, sowie eine im Verbund realisierte autonome Parkfunktion. Die Radarsegmentierung der (statischen) Umgebung wird durch ein Radar-spezifisches neuronales Netzwerk RadarNet erreicht. Diese Segmentierung ermöglicht die Entwicklung des semantischen Radar Graph-SLAM SERALOC. Auf der Grundlage der semantischen Radar SLAM Karte wird eine beispielhafte autonome Parkfunktionalität in einem realen Versuchsträger umgesetzt. Entlang eines aufgezeichneten Referenzfades parkt die Funktion ausschließlich auf Basis der Radar Wahrnehmung mit bisher unerreichter Positioniergenauigkeit. Im ersten Schritt wird ein Datensatz von 8.2 · 10^6 punktweise semantisch gelabelten Radarpunktwolken über eine Strecke von 2507.35m generiert. Es sind keine vergleichbaren Datensätze dieser Annotationsebene und Radarspezifikation öffentlich verfügbar. Das überwachte Training der semantischen Segmentierung RadarNet erreicht 28.97% mIoU auf sechs Klassen. Außerdem wird ein automatisiertes Radar-Labeling-Framework SeRaLF vorgestellt, welches das Radarlabeling multimodal mittels Referenzkameras und LiDAR unterstützt. Für die kohärente Kartierung wird ein Radarsignal-Vorfilter auf der Grundlage einer Aktivierungskarte entworfen, welcher Rauschen und andere dynamische Mehrwegreflektionen unterdrückt. Ein speziell für Radar angepasstes Graph-SLAM-Frontend mit Radar-Odometrie Kanten zwischen Teil-Karten und semantisch separater NDT Registrierung setzt die vorgefilterten semantischen Radarscans zu einer konsistenten metrischen Karte zusammen. Die Kartierungsgenauigkeit und die Datenassoziation werden somit erhöht und der erste semantische Radar Graph-SLAM für beliebige statische Umgebungen realisiert. Integriert in ein reales Testfahrzeug, wird das Zusammenspiel der live RadarNet Segmentierung und des semantischen Radar Graph-SLAM anhand einer rein Radar-basierten autonomen Parkfunktionalität evaluiert. Im Durchschnitt über 42 autonome Parkmanöver (∅3.73 km/h) bei durchschnittlicher Manöverlänge von ∅172.75m wird ein Median absoluter Posenfehler von 0.235m und End-Posenfehler von 0.2443m erreicht, der vergleichbare Radar-Lokalisierungsergebnisse um ≈ 50% übertrifft. Die Kartengenauigkeit von veränderlichen, neukartierten Orten über eine Kartierungsdistanz von ∅165m ergibt eine ≈ 56%-ige Kartenkonsistenz bei einer Abweichung von ∅0.163m. Für das autonome Parken wurde ein gegebener Trajektorienplaner und Regleransatz verwendet.

In this article, we investigate the feasibility of a high angular resolution method based on deep learning for estimating the angle of arrival (AoA) using an automotive frequency-modulated continuous-wave (FMCW) multiple-input multiple-output (MIMO) radar. The deep learning approach takes advantage of the 2-D signal structure from the range-Doppler (RD) maps to determine AoA of targets. To achieve this, we use a neural network architecture that is based on the frequency-representation module of the DeepFreq model. We call it FRNet12, since it uses 12 virtual antennas, which are derived from three transmit and four receive antennas using our FMCW MIMO radar. Furthermore, we propose a cascaded neural network system to further improve the performance of the FRNet12 model. This system consists of an extrapolation neural network (ETPNet) and FRNet18. The ETPNet extrapolates six additional samples from the 12 virtual antenna inputs, and the output is then used to train the FRNet18. The cascaded system provides an improvement of 33 % in angular resolution compared to FRNet12. Additionally, it maintains the probability of resolution (PoR) at nearly 100 % , even when two targets with different amplitudes are within the theoretical angular resolution region. The proposed method is verified using simulation and measurement data from a 77-GHz FMCW MIMO radar with three transmit and four receive antennas. The results of this research demonstrate the potential of applying deep learning to estimate the AoA in an automotive radar system, and the proposed cascaded neural network system represents a significant improvement over the FRNet12 model. Version of record

A 240-GHz 4-transmitter (TX) 4-receiver (RX) 2-D multiple-input–multiple-output (MIMO) frequency-modulated continuous-wave (FMCW) radar transceiver was designed in a 130-nm SiGe bipolar CMOS (BiCMOS) technology with fT / fmax of 250/370 GHz using patch antennas on a single Undefined control sequence \ttimes mm2 chip. The differential RF inputs, located at the emitters of the direct conversion mixer quad transistors of each RX channel are dc-coupled and current-matched to the differential on-chip patch antenna. At 240 GHz, measurements reveal an RX conversion gain of 24.8 dB, a single-sideband noise figure of 14.9 dB, and an output power of − 2 dBm. Version of record

International audience; In this paper, a method to characterize complex permittivity of dielectric is presented. The back-scattered signal from a resonant scatterer placed in contact with the dielectric is used to estimate the dielectric properties. The proposed method was tested in simulation and validated in practice using different dielectric samples and different dielectric thicknesses. This method is wireless, non-destructive, with no restriction on the sample thickness and is done using a VNA and an antenna. Discussions on the geometry of the resonator as well as the calibration step is proposed in order to improve the sensing capability of the approach. Monte-Carlo simulation has been performed to define a confidence interval for the values extracted with the proposed approach in the range 1 to 3.5 for permittivity and 0 to 0.2 for tan δ with a SNR of 20dB. For example, a permittivity εr = 3.54 ± 0.06 and tan δ = 0.0024 ± 0.003 has been measured for Rogers RO4003C and εr = 2.31 ± 0.05 and tan δ = 0.004 ± 0.002 for Duroid RT5880 with different thicknesses. It is also shown in simulation that the approach is compatible with materials having loss tangents up to 0.5, and real permittivity up to 10.

Publisher Copyright: IEEE We consider the problem of monostatic radar sensing with orthogonal frequency-division multiplexing (OFDM) joint radar-communications (JRC) systems in the presence of phase noise (PN) caused by oscillator imperfections. We begin by providing a rigorous statistical characterization of PN in the radar receiver over multiple OFDM symbols for free-running oscillators (FROs) and phase-locked loops (PLLs). Based on the delay-dependent PN covariance matrix, we derive the hybrid maximum-likelihood (ML)/maximum a-posteriori (MAP) estimator of the deterministic delay-Doppler parameters and the random PN, resulting in a challenging high-dimensional nonlinear optimization problem. To circumvent the nonlinearity of PN, we then develop an iterated small angle approximation (ISAA) algorithm that progressively refines delay-Doppler-PN estimates via closed-form updates of PN as a function of delay-Doppler at each iteration. Moreover, unlike existing approaches where PN is considered to be purely an impairment that has to be mitigated, we propose to exploit PN for resolving range ambiguity by capitalizing on its delay-dependent statistics (i.e., the range correlation effect), through the formulation of a parametric Toeplitz-block Toeplitz covariance matrix reconstruction problem. Simulation results indicate quick convergence of ISAA to the hybrid Cramér-Rao bound (CRB), as well as its remarkable performance gains over state-of-the-art benchmarks, for both FROs and PLLs under various operating conditions, while showing that the detrimental effect of PN can be turned into an advantage for sensing.

This paper presents a silicon micromachined reconfigurable beam-steering frontend chip in which multiple components are integrated within a single silicon chip. The size o the whole frontend chip is 14.3×20×1.2 mm3. The signal path includes a novel switching circuit, a beam-steering network, two power-dividing networks, and a 2×8 array antenna. The presented frontend chip is able to create two frequency steerable beam-shapes. The switching circuit determines which beam shape to be radiated. The first beam-shape is a steerable broad fan-beam with reduced sidelobe level (SLL) in the H-plane, and the second is a steerable notched fan-beam. The measured SLL of the broad beam is better than 18.5 dB in the H-plane, and the measured notch depth is better than 24 dB in the E-plane pattern. Both radiation patterns can be steered by frequency and move from -20 degrees at 238 GHz to +20 degrees at 248 GHz.The measured return loss of the fabricated device is better than 15 dB at 220-265 GHz, which is equal to almost 18.6 % fractional bandwidth. QC 20230630

This thesis focuses on the reduction of phase noise caused by the bumper reflection in an FMCW radar system. The impact of phase noise depends on the magnitude of the reflector which is quite large in case of the bumper. That raises the noise floor of the overall system which limits target detection. To reduce the phase noise level the stationary point concentration algorithm is used. It allows a fully digital modification of the received IQ signals that results in a noise shaping into the DC frequency bin. In this thesis several properties of the algorithm are being investigated. These include its limitations with distributed and multiple targets. Moreover the required parameter estimation precision is analyzed. A new property of the algorithm was found which states that its mitigation capability improves with lower overall noise level. To demonstrate the findings a 77 GHz digital radar demonstrator is built to test the algorithm on a practical system with synthetic phase noise. The demonstrator takes advantage of the fast sampling rates of modern high-speed DACs which allow direct synthesis of the signal samples up to 4 GHz. The chirps are converted to/from RF using 77 GHz mixer modules. With this setup arbitrary chirp parameters and the introduction of synthetic noise into the signal samples is possible. Ziel dieser Masterarbeit ist es das Phasenrauschen, welches aufgrund der Stoßstangenreflexion in einem FMCW Radar entsteht, zu reduzieren. Der Einfluss von Phasenrauschen ist abhängig von der Stärke der Reflexion, welche im Falle der Stoßstange relativ groß ist. Dadurch steigt das Rauschlevel des gesamten Systems, was wiederum die Möglichkeiten zur Detektion von Zielen beeinflusst. Um die Reduktion zu erreichen, wird der Algorithmus „Stationäre Punkt Konzentration“ verwendet. Dieser erlaubt eine digitale Modifikation des empfangenen IQ Signales welche das Rauschen in DC Frequenz formt. In dieser Arbeit werden mehrere Eigenschaften des Algorithmus analysiert. Dazu gehören Limitierungen mit ausgedehnten und mehreren Zielen. Außerdem wird untersucht mit welcher Genauigkeit die Parameter für den Algorithmus gemessen werden müssen. Eine neue Eigenschaft wurde gefunden, welche besagt, dass das Reduktionspotential mit niedrigerem Rauschlevel steigt. Um die Ergebnisse zu zeigen wurde ein 77GHz Radar Demonstrator gebaut, dessen Signale von einem digitalen Prozessor erzeugt werden. Das erlaubt die Verwendung von synthetischem Phasenrauschen. Author Franz Lehner, BSc Abweichender Titel laut Übersetzung der Verfasserin/des Verfassers Masterarbeit Universität Linz 2023