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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.

Nyquist Folding Receiver (NYFR) is a perceptron structure that realizes a low probability of intercept (LPI) signal analog to information. Aiming at the problem of LPI radar signal receiving, the time domain, frequency domain, and time-frequency domain problems of signals intercepted by NYFR structure are studied. Combined with the time-frequency analysis (TFA) method, a radar recognition scheme based on deep learning (DL) is introduced, which can reliably classify common LPI radar signals. First, the structure of NYFR and its characteristics in the time domain, frequency domain, and time and frequency domain are analyzed. Then, the received signal is then converted into a time-frequency image (TFI). Finally, four kinds of DL algorithms are used to classify LPI radar signals. Simulation results demonstrate the correctness of the NYFR structure, and the effectiveness of the proposed recognition method is verified by comparison experiments.

International audience; In this paper, a novel method to improve the readability of chipless tags is presented. When the EM wave falls upon the tag's surface at normal incidence or with a known incidence, the proposed approach permits to obtain a higher signal-to-noise ratio (SNR) of resonators whose orientation with respect to the normal to the plane in which the tag is positioned is unknown and thus, to increase the reading distance of the tag. It introduces a technique based on the detection of this tag orientation in relation to the antennas; a projection of the signals will allow to correct this misalignment in order to always be able to decode the tag identifier on a signal that corresponds to an ideal alignment between the tag and the antennas. With this principle, it is also possible to separate the resonance mode of the resonator that is connected to the identifier from other parasitic resonance modes that may appear due to a possible misalignment between the tag and the antenna. This principle is applied to loop resonators commonly used in chipless RFID which can present strong parasitic modes of resonance. Also, this method makes the reading orientation invariant, allowing to read the resonator whatever its orientation with respect to the normal to the plane in which the tag is positioned. Likewise, the proposed method can be also used to sense this resonator orientation. This method requires a dual-polarization antenna and a 2-ports vector network analyzer (VNA). Results are validated in simulation and with real-environment measurements for a single resonator as well as for multiple resonators, as it is classically used in the chipless RFID technology. Measurements in a harsh environment, as well as comparisons with classical techniques such as time gating, are presented. A study on the improvment of the read range is achieved to highligth the potential of the proposed technique. A reading distance of more than 80cm was thus obtained, which represents 20cm more than with the classic use of a post-processor based on time gatting. Finally, the principle of the measurement is generalized by applying it to a tag with a ground plane and considering that the antenna is not necessarily placed in normal incidence with respect to the plane of the tag.

The integration of wireless sensors into the automotive industry is making vehicles increasingly safe and autonomous. At present, vehicles are equipped with external sensors that facilitate maneuverability and braking and internal sensors that detect presence, seat belt state, and occupant vital signs. Early detection of dysfunctional breathing patterns can reduce the risk of accidents caused by drowsiness or fatigue. In this regard, this work presents an MIMO FMCW Radar System able to distinguish dysfunctional breathing patterns from human torso motion. The torso has been divided into six regions, and the amplitude of the elongation and the frequency of motion of each region have been measured during breathing. An elongation map is then constructed in order to graphically show the dysfunctional breathing patterns. The signal processing includes a peak search algorithm to detect elongation amplitude and a bandpass frequency filter to minimize dc components, random driver motion, and vehicle vibration. A torso elongation emulator phantom has been assembled with materials of skin-like relative permittivity and dc motors for calibration and validation of basic breathing patterns. Finally, the signal of the system is compared with that of a commercial respiration belt, and then the system is tested by performing measurements on people. The MIMO radar system is able to measure, differentiate, and classify patterns associated with dysfunctional breathing such as hyperventilation syndrome and thoracic dominant breathing. This work was supported in part by the Spanish “Comision Interministerial de Ciencia y Tecnologia” (CICYT) under Project PID2019-107885GB-C31 and Project MDM2016- 0600, in part by the Catalan Research Group under Grant 2017 SGR 219, and in part by the “Secretaría Nacional de Educación Superior, Ciencia, Tecnología e Innovación” (SENESCYT) from the Ecuadorian Government

The ambiguity problem of targets in Doppler beam sharpening (DBS) with forward-looking radar is considered. While DBS is proposed earlier to improve the angular resolution of the radar while keeping the antenna aperture size limited, such a solution suffers from ambiguities in the case of targets positioned symmetrically with respect to the platform movement. To address this problem, an approach named unambiguous Doppler-based forward-looking multiple-input multiple-output (MIMO) radar beam sharpening scan (UDFMBSC) is proposed, based on the combination of MIMO processing and DBS. The performance of the proposed method is compared to existing approaches using simulated data with point-like and extended targets. The method is successfully verified using experimental data.

Linked to the increasing availability of datasets for radar-based human activity recognition (HAR), in this Student Highlights contribution, we report on a classification project that a group of 23 graduate students performed at TU Delft. The students were asked to work in groups of 2-3 members and to use the publicly available University of Glasgow dataset to develop the best classification pipeline as possible. This involved development and justification of both choices for the preprocessing techniques on the radar data (e.g., time-frequency distributions and cleaning of the signatures), and for the classification algorithms (e.g., the type of the algorithm, the hyperparameters' selection, the training-validation-testing split). While this student activity was performed at a small scale and with educational rather than research aims, we are happy to report it to the AESS readership, as we believe that such initiatives with open datasets sharing and classification algorithm benchmarking are beneficial for the wider radar research community. Furthermore, a list of publicly available datasets for radar-based HAR that can be used for similar initiatives is also reported in this article.

We observed the density of newly fallen snow with K-band vertical radar in Niigata prefecture, Japan. Focusing on the effect of riming on the density of newly fallen snow, we investigated the characteristics that appear in vertical radar observation data when riming growth is dominant: an increase in fall velocity and a decrease in the vertical gradient of radar reflectivity. In the case of graupel dominance, the increase in fall velocity was observed, and in the case of graupel-like snow dominance, the decrease in the vertical gradient of the radar reflectivity was observed.

In the present study, we analyzed the particle size distribution (PSD) of falling volcanic ash particles collected for a total of 205 eruptions in 2018-2020 at Sakurajima volcano in Kagoshima Prefecture, Japan. PSD data were measured with the DPRI optical disdrometer network. Assuming the gamma PSD model, we examined the relationships between each of the gamma PSD parameters calculated by the complete moment method. It was shown that there were good correlations between each of the gamma PSD parameters, which might be one of the characteristics of falling volcanic ash particles. We confirmed from the normalized gamma PSD analysis that the normalized intercept parameter and mass-weighted mean diameter are suitable for estimating the ash fall rate. The results of the present study provide essential information for studying microphysical processes in volcanic ash clouds and improving ash transport and sedimentation models. We also derived theoretical power law relationships between pairs of integrated PSD parameters, which can be applied to weather radar monitoring of ash fall distributions.

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.

The network extended not just internationally but also throughout a broad variety of application areas in this age, with healthcare being one of the most well-known and vital industries. Improvements in healthcare are possible if we start using the popular internet of things (IoT) technology as a key instead of focusing on other disciplines. Wireless body area network (WBAN) is a field in which we communicate with a network of human people and medical equipment that may be used in conjunction with internet of things technology to perform any function. Additional features for software defined networks will be added in the future. In the event of a critical crisis, the suggested suggestions will be to take care of the patient's life. Because the fitted equipment keeps a lot better eye on the patient than previously advised methods. This study combines WBAN, IoT, and software defined network (SDN) to make sense in the healthcare field.

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.

V okviru magistrske naloge sem razvil metodo za detekcijo intenzivnih neviht na območju Slovenije. Metoda detektira intenzivne konvektivne celice na podlagi meritev meteorološkega radarja in razelektritev. Uporabljena je bila rutina pysteps, ki temelji na tem, da v polju radarske odbojnosti najprej išče zaključena območja nad določenim pragom radarske odbojnosti, nato pa preko dodatnih kriterijev iz zaključenih območij določi konvektivne celice. Optimizacija in verifikacija rezultatov je bila izvedena na konvektivnih sezonah od maja do septembra v letih od 2020 do 2022. Rezultati metode so bili optimizirani in verificirani na meritvah toče na padavinskih in klimatoloških postajah Agencije Republike Slovenije za okolje (ARSO) in na podatkih o intervencijah Uprave Republike Slovenije za zaščito in reševanje (URSZR), ki vsebujejo intervencije, ki so se zgodile zaradi toče, vetroloma in poplav meteorne vode. V primeru podatkov o intervencijah sta bili optimizacija in verifikacija izvedeni le na izbranih 13 območjih, kjer je prišlo do zadostnega števila javljenih dogodkov. Iz rezultatov so bili izračunani verifikacijski indeksi POD (angl. Probability of detection), FAR (angl. False alarm rate), CSI (angl. Critical success index) in BIAS, ki bazirajo na kontingenčni tabeli. Preko teh indeksov je bila nato ovrednotena uspešnost metode in so bili določeni optimalni parametri. Pri verifikaciji algoritma na meritvah toče na meteoroloških postajah so pri optimalnih parametrih verifikacijski indeksi dosegli vrednost POD = 0,39, FAR = 0,82, CSI = 0,14 in BIAS = 2,20, pri verifikaciji na podatkih o intervencijah toče, vetroloma in poplav meteorne vode pa so verifikacijski indeksi znašali POD = 0,29, FAR = 0,61, CSI = 0,20 in BIAS = 0,73. Posebej je bilo analiziranih tudi nekaj nevihtnih dni, za katere je ARSO izdal poročilo o neurjih. Za te dni je verifikacijski indeks CSI znašal med 0,10 in 0,33 za verifikacijo toče na meteoroloških postajah, pri verifikaciji na podatkih o intervencijah toče, vetroloma in poplav meteorne vode pa je indeks dosegal vrednosti med 0,25 in 0,80. Razvita metoda se pri detekciji intenzivnih neviht v splošnem obnese dobro, rezultati verifikacije pa so primerljivi z rezultati podobnih študij, narejenih v Sloveniji in tujni. In this master’s thesis, I developed a method for detecting intense thunderstorms in the area of Slovenia. The method detects intense convective cells based on meteorological radar measurements and lightning discharges. The pysteps routine was used, which searches for closed areas above a certain threshold of radar reflectivity in the radar reflectivity field, and then determines convective cells from the closed areas using additional criteria. The optimization and verification of the results were carried out for the convective seasons from May to September in the years from 2020 to 2022. The results of this method were optimized and verified on the hail measurements of the precipitation and climatological stations of the Slovenian Environment Agency (ARSO) and on the intervention data of the Administration for Civil Protection and Disaster Relief (URSZR), where the events of hail, severe winds and floods of stormwater were jointly analyzed. In the case of data on interventions, optimization and verification were performed only in selected 13 areas where a sufficient number of reported events occurred. From the results, the verification indices POD (Probability of detection), FAR (False alarm rate), CSI (Critical success index) and BIAS were calculated, which are based on a contingency table. Using these indices, the performance of the method was then evaluated and the optimal parameters were determined. When verifying the method on hail measurements at meteorological stations with optimal parameters, the verification indices reached the value POD = 0.39, FAR = 0.82, CSI = 0.14 and BIAS = 2.20, and when verifying data on interventions related to hail, severe winds and stormwater floods, the verification indices amounted to POD = 0.29, FAR = 0.61, CSI = 0.20 and BIAS = 0.73. A few stormy days for which ARSO issued a storm report were also analyzed separately. For these days, the CSI verification index was between 0.10 and 0.33 for the verification of hail at meteorological stations, while the index reached values between 0.25 and 0.80 when verifying data on hail, severe winds and stormwater floods interventions. The developed method generally performs well in the detection of intense thunderstorms, and the verification results are comparable to the results of other studies performed in Slovenia or abroad.

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.

Resumen La polarimetría con radar de apertura sintética (SAR) es una técnica conocida en teledetección por sus aplicaciones en el monitoreo de cambios sobre la superficie terrestre y en cuerpos de agua. El objetivo de la polarimetría radar es caracterizar la superficie terrestre analizando las propiedades de la señal que se dispersa cuando se utilizan diferentes combinaciones de polarización de las antenas transmisoras y receptoras, definidas como canales polarimétricos. La utilización de la polarización cruzada (vertical/horizontal) se mostró eficiente para estimar la extensión de las inundaciones producidas entre el 22 y el 25 de julio de 2021 en la cuenca del río Frío (Costa Rica). Para ello se utilizaron imágenes captadas por el sensor Sentinel 1A, de la Agencia Espacial Europea, mediante un flujo de procesamiento y una composición RGB posterior que permitió establecer una clara distinción entre las zonas inundadas de los cuerpos de agua permanentes. La utilización de imágenes ópticas para el mapeo de inundaciones está limitada por las condiciones climáticas, especialmente por la presencia de nubes. Las mediciones con SAR son independientes de la hora del día y de las condiciones climáticas, proporcionando valiosa información para el monitoreo de inundaciones (Horritt, 2006, p 187). Además, la extensión de las inundaciones es un factor crítico para la calibración y validación de modelos hidráulicos. La extensión de las inundaciones puede usarse también para evaluación y gestión del riesgo, y pueden beneficiarse de ello rescatistas durante las inundaciones (Corbley, 1999, p 1). Abstract Synthetic Aperture Radar (SAR) polarimetry is a remote sensing technique known for being applied to monitoring of land surface and water bodies. Polarimetry aims at charactering the land surface through the analysis of its response under different combinations of transmitting and receiving antennas polarization, called polarimetric channels. The use of cross-polarization (vertical/horizontal) proved to be efficient for estimating the extent of the floods between July 22nd and July 30th in the Frío river basin (Costa Rica). To that end, Sentinel-1 images from the European Space Agency have been used, with a workflow process and RGB composition that allowed to draw a clear distinction between flooded and permanent water bodies. The use of optical imagery for flood mapping is limited by weather conditions, specially by the presence of clouds. SAR measurements are independent of daytime and weather conditions, providing valuable information for monitoring flood events. Furthermore, flood extent is a critical factor for the calibration and validation of hydraulic models (Horritt, 2006). Flood extent can be also used for damage assessment and risk management and can be of benefit to rescuers during flooding (Corbley, 1999). Resumo A polarimetria por radar de abertura sintética (SAR) é uma técnica conhecida em sensoriamento remoto por suas aplicações no monitoramento de mudanças na superfície terrestre e em corpos d'água. O objetivo da polarimetria de radar é caracterizar a superfície terrestre analisando as propriedades do sinal que é espalhado quando são utilizadas diferentes combinações de polarização das antenas transmissoras e receptoras, definidas como canais polarimétricos. O uso da polarização cruzada (vertical/horizontal) mostrou-se eficiente para estimar a extensão das inundações produzidas entre 22 e 25 de julho de 2021 na bacia do rio Frío (Costa Rica). Para isso, foram utilizadas imagens captadas pelo sensor Sentinel 1A da Agência Espacial Europeia, através de um fluxo de processamento e posterior composição RGB que permitiu distinguir claramente as áreas inundadas dos corpos d'água permanentes. O uso de imagens ópticas para mapeamento de inundações é limitado pelas condições climáticas, especialmente pela presença de nuvens. As medições de SAR são independentes da hora do dia e das condições meteorológicas, fornecendo informações valiosas para o monitoramento de enchentes (Horritt, 2006, p.187). Além disso, a extensão da inundação é um fator crítico para a calibração e validação de modelos hidráulicos. A extensão das inundações também pode ser usada para avaliação e gestão de risco e pode beneficiar os socorristas durante as inundações (Corbley, 1999, p. 1).

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.

Radar-based gesture recognition can play a vital role in autonomous vehicles’ interaction with vulnerable road users (VRUs). However, in automotive scenarios the same gesture produces strongly differing radar responses owed to the wide range of variations such as position, orientation, or ego-motion. Since including all kinds of modifications in a measured dataset is laborious, gesture simulations alleviate the measurement effort and increase the robustness against edge and corner cases. Hence, this paper presents a flexible geometric human target model allowing the direct introduction of a wide range of modifications, while it facilitates the handling of shadowing effects and multiradar constellations. Using the proposed simulation model, a dataset recorded with a radar sensor network consisting of three chirp sequence (CS) radars is resimulated based on motion data simultaneously captured with a stereo video system. Completely substituting the measured by simulated data for training, a convolutional neural network (CNN) classifier still achieves 80.4% cross-validation accuracy on a challenging gesture set, compared to 89.4% for training on measured data. Moreover, using simulated data the classifier is shown to successfully generalize to new scenarios not observed in measurements., acceptedVersion

This article considers the detection and tracking of weak targets in multiple pulse repetition frequency (multi-PRF) radars using the track-before-detect (TBD) technique. By exploring the measurement independence among different PRFs, we decompose the joint multi-PRF and multiframe optimization problem into several lower dimensional maximizations, each of which corresponds to an intra-PRF multiframe processing. An efficient two-stage TBD procedure with a parallel structure is proposed for the multi-PRF radars. Specifically, in the first stage, a constraint inequality is derived analytically and used to decouple the measurement ambiguities from the nonlinear conversion relationship between polar and Cartesian coordinates. The intra-PRF multiframe integration can then be carried out concurrently using the ambiguous measurements and the target-like measurement plot sequences with different ambiguities are extracted for different PRFs. In the second stage, a covariance combination fusion-based inter-PRF joint disambiguation and estimation algorithm is proposed to solve the ambiguities of the plot sequences and output high-Accuracy target tracks. Simulation experiments show that the proposed algorithm can provide a good detection performance and higher tracking accuracy with much lower complexity. This work was supported in part by the National Natural Science Foundation of China under Grant 61771110, Grant U19B2017, and Grant 61871103, in part by the Fundamental Research Funds of Central Universities under Grant ZYGX2020ZB029, in part by Chang Jiang Scholars Program and the 111 Project under Grant B17008, and in part by the China Scholarship Council.

Microwave imaging has been investigated for medical applications such as stroke and breast imaging. Current systems typically rely on bench-top equipment to scan at a variety of antenna positions. For dynamic imaging of moving structures, such as the cardiovascular system, much higher imaging speeds are required than what has thus far been reported. Recent innovations in radar-on-chip technology allow for simultaneous high speed data collection at multiple antenna positions at a fraction of the cost of conventional microwave equipment, in a small and potentially portable system. The objective of the current work is to provide proof of concept of dynamic microwave imaging in the body, using radar-on-chip technology.Arrays of body-coupled antennas were used with nine simultaneously operated coherent ultra-wideband radar chips. Data were collected from the chest and thigh of a volunteer, with the objective of imaging the femoral artery and beating heart. In addition, data were collected from a phantom to validate system performance. Video data were constructed using beamforming.The location of the femoral artery could successfully be resolved, and a distinct arterial pulse wave was discernable. Cardiac activity was imaged at locations corresponding to the heart, but image quality was insufficient to identify individual anatomical structures. Static and differential imaging of the femur bone proved unsuccessful.Using radar chip technology and an imaging approach, cardiovascular activity was detected in the body, demonstrating first steps towards biomedical dynamic microwave imaging. The current portable and modular system design was found unsuitable for static in-body imaging.This first proof of concept demonstrates that radar-on-chip could enable cardiovascular imaging in a low-cost, small and portable system. Such a system could make medical imaging more accessible, particularly in ambulatory or long-term monitoring settings.

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.

ilustraciones, fotografías Este proyecto de investigación busca realizar un enlace inalámbrico de un radar GPR-UWB con un dispositivo de procesamiento, con el fin de facilitar su utilización en campo, este dispositivo se utilizará en el desminado humanitario. La comunicación inalámbrica se logra mediante el diseño de un embebido el cual comunica el GPR-UWB con un portátil que procesa los datos en Matlab. Luego se realizan mediciones de la interferencia del enlace en los A-scan generados por el radar de penetración de suelos y se analiza la viabilidad de los radio enlaces cerca de los radares de penetración de suelos. Esta investigación fue financiado por el proyecto de MEDICI del grupo de investigación EMC-UN y dentro de las instalaciones de la universidad Nacional. (Texto tomado de la fuente) This research project seeks to make a wireless link of a GPRUWB radar with a proces- sing device, in order to facilitate its use in the field, this device will be used in humanitarian demining. The project also seeks to explore processing techniques that can improve the de- tection and characterization of explosive devices. This research was financed by the MEDICI project of the EMC-UN research group and within the facilities of the National University. Wireless communication was achieved through the design of an embedder which communicates the GPR-UWB with a port. Atil that processes the data in Matlab. Link interference measurements are then made on A-scans generated by the ground penetrating radar and the feasibility of radio links near ground penetrating radars is analyzed. Maestría Magíster en Ingeniería - Ingeniería Electrónica Comunicaciones inalámbricas y procesamiento de señales

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.

International audience; Intact tropical rainforests have been exposed to severe droughts in recent decades, which may threaten their integrity, their ability to sequester carbon, and their capacity to provide shelter for biodiversity. However, their response to droughts remains uncertain due to limited high-quality, long-term observations covering extensive areas. Here, we examined how the upper canopy of intact tropical rainforests has responded to drought events globally and during the past 3 decades. By developing a long pantropical time series (1992 to 2018) of monthly radar satellite observations, we show that repeated droughts caused a sustained decline in radar signal in 93%, 84%, and 88% of intact tropical rainforests in the Americas, Africa, and Asia, respectively. Sudden decreases in radar signal were detected around the 1997–1998, 2005, 2010, and 2015 droughts in tropical Americas; 1999–2000, 2004–2005, 2010–2011, and 2015 droughts in tropical Africa; and 1997–1998, 2006, and 2015 droughts in tropical Asia. Rainforests showed similar low resistance (the ability to maintain predrought condition when drought occurs) to severe droughts across continents, but American rainforests consistently showed the lowest resilience (the ability to return to predrought condition after the drought event). Moreover, while the resistance of intact tropical rainforests to drought is decreasing, albeit weakly in tropical Africa and Asia, forest resilience has not increased significantly. Our results therefore suggest the capacity of intact rainforests to withstand future droughts is limited. This has negative implications for climate change mitigation through forest-based climate solutions and the associated pledges made by countries under the Paris Agreement.

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 %.

Human-based object detection in synthetic aperture RADAR (SAR) imagery is complex and technical, laboriously slow but time critical—the perfect application for machine learning (ML). Training an ML network for object detection requires very large image datasets with imbedded objects that are accurately and precisely labeled. Unfortunately, no such SAR datasets exist. Therefore, this paper proposes a method to synthesize wide field of view (FOV) SAR images by combining two existing datasets: SAMPLE, which is composed of both real and synthetic single-object chips, and MSTAR Clutter, which is composed of real wide-FOV SAR images. Synthetic objects are extracted from SAMPLE using threshold-based segmentation before being alpha-blended onto patches from MSTAR Clutter. To validate the novel synthesis method, individual object chips are created and classified using a simple convolutional neural network (CNN); testing is performed against the measured SAMPLE subset. A novel technique is also developed to investigate training activity in deep layers. The proposed data augmentation technique produces a 17% increase in the accuracy of measured SAR image classification. This improvement shows that any residual artifacts from segmentation and blending do not negatively affect ML, which is promising for future use in wide-area SAR synthesis. Outstanding Thesis Major, United States Air Force Approved for public release. Distribution is unlimited.

In January 2021, the DOD released its first Counter-Small Unmanned Aircraft Systems Strategy to address the growing risk to military personnel, facilities, and assets posed by the rapid technological advancement and proliferation of sUAS. Existing counter-drone capabilities—heavily reliant on electronic warfare to disrupt the communication link between user and device—no longer address an evolving threat that includes autonomous drones, COTS technology, and an increasing number of drones in the airspace that can overwhelm a C-sUAS operator. To counter the increasingly complex small drone threat, the Army-led Joint Counter-sUAS Office is pursuing materiel and non-materiel solutions for its new system-of-systems approach. One vexing C-sUAS challenge involves radar detection systems discriminating some sUAS from other flying objects, like birds, due to their comparable size, slow movement, and low altitude. Inaccurate or inefficient sUAS classification using radar data can be a force protection threat due to the limited number of electro-optical sensors and human operators for classification at-scale. This thesis uses bird and drone radar track data from two different training environments to explore hidden structure in the data, develop independent unsupervised and supervised learning models using the two datasets, and experiment with data sampling and feature engineering to improve upon model robustness to different environments and dynamic environmental conditions. Lieutenant Colonel, United States Army Approved for public release. Distribution is unlimited.

Soil moisture estimates at high spatial and temporal resolution are of great value for optimizing water and agricultural management. To fill the gap between local ground observations and coarse spatial resolution remote sensing products, we use Soil Moisture Active Passive (SMAP) and Sentinel-1 data together with a unique data set of ground-based soil moisture estimates by cosmic ray neutron sensors (CRNS) and capacitance probes to test the possibility of downscaling soil moisture to the sub-kilometre resolution. For a high-latitude study area within a highly heterogeneous landscape and diverse land use in Denmark, we first show that SMAP soil moisture and Sentinel-1 backscatter time series correlate well with in situ CRNS observations. Sentinel-1 backscatter in both VV and VH polarizations shows a strong correlation with CRNS soil moisture at higher spatial resolutions (20–400 m) and exhibits distinct and meaningful signals at different land cover types. Satisfactory statistical correlations with CRNS soil moisture time series and capacitance probes are obtained using the SMAP Sentinel-1 downscaling algorithm. Accounting for different land use in the downscaling algorithm additionally improved the spatial distribution. However, the downscaling algorithm investigated here does not fully account for the vegetation dependency at sub-kilometre resolution. The study suggests that future research focussing on further modifying the downscaling algorithm could improve representative soil moisture patterns at a fine scale since backscatter signals are clearly informative. Highlights. Backscatter produces informative signals even at high resolutions. At the 100 m scale, the Sentinel-1 VV and VH polarizations are soil moisture dependent. The downscaling algorithm is improved by introducing land-cover-dependent clusters. The downscaled satellite and CRNS soil moisture agree best at the agricultural site.

The future contributions of the Antarctic Ice Sheet to sea level rise will depend on the evolution of its surface mass balance (SMB), which could amplify/dampen mass losses increasingly observed at the ice sheet's edge. In situ constraints of SMB over annual-to-decadal timescales consist mostly of firn/ice cores that have a surface footprint $\sim$cm$^{2}$. SMB constraints also come from climate models, which have a higher temporal resolution but a larger surface footprint of several km$^{2}$. We use ice-penetrating radar data to obtain an intermediate spatial and temporal resolution SMB record over three ice rises along the Princess Ragnhild Coast. The co-located ice cores allow us to obtain absolute radar-derived SMB rates at a multi-annual-to-decadal temporal resolution. By comparing the ice core SMB measurements and the radar-derived SMB records, we determine that pointwise measurements of SMB are representative of a small surface area, $\sim 200-500$ m radius extending from the ice core drill site for the ice rises studied here, and that the pointwise measurements are systematically 7–15 cm w.e. a$^{-1}$ lower than the mean SMB value calculated for the whole ice rises. However, ice core records are representative of an entire ice rise's temporal variability at the temporal resolution examined.

In-band full-duplex (IBFD) technology has the potential to not only double the communication throughput but also enable additional capabilities. The fusion of radar and communication subsystems is an excellent example of how IBFD facilitates integration of radar functionality into a communication system. Developing and analysis of a joint IBFD radar-communication (RadCom) system is at the core of this article. This system derives the range-Doppler image from the state of an adaptive filter, which already exists in a typical IBFD transceiver. Our approach is waveform-independent and reuses the radio frequency (RF) front-end while it requires little additional digital logic. The proposed system is prototyped to assess the modem-radar coexistence in a real-world IBFD communication link budget. Employing the prototyped system, we quantify the additional required logic resources and investigate whether such a RadCom approach dictates a tradeoff between the two intended functionalities. The experimental results show that the proposed solution enables a communication system to detect targets within 20 m while maintaining an IBFD link with another communication node, info:eu-repo/semantics/published