Search

Both sets of Martian polar layered deposits (PLDs) display semi‐rhythmic reflector stratigraphy in radargrams. The southern PLDs (SPLD), among other peculiarities, also contain widespread, distinctive deposits with few to no reflections that commonly occur at or near the top of the stratigraphic column. Here, we study the low reflectivity zones (LRZs) to determine whether they exhibit radar properties indicative of non‐water ice. We find that the previously reported CO2‐ice LRZ exhibits a distinct radar signature compared to all other LRZs. We also found behavior that suggests that the other LRZs may contribute to increased radar signal attenuation, which does not support a CO2 ice composition. We observe many instances of LRZs comprising the entire SPLD column and find that the radar attenuation varies across different LRZs, suggesting unique properties within each of these deposits. Variable radar attenuation of LRZs may be connected to differences in bulk compositions, radar‐sensitive roughness characteristics, and/or distributions of stratigraphic materials. Plain Language Summary: From orbital data, we know that both of the Martian polar caps contain widespread layers of dusty water ice. However, radar data, in particular, reveal that the southern cap also contains large sections of layer‐free material. Previous studies have established that one of these occurrences of layer‐free material is composed of carbon dioxide ice. We looked at the remaining occurrences of layer‐free zones in the southern cap and found that they do not show the same radar signatures as those used to deduce the composition of the carbon dioxide unit. These other layer‐free units are often associated with decreased signal strength compared to regions where layers are present. We also see variations in the signal strength across these layer‐free units, suggesting subtle differences in their composition, material distribution, or surface roughness of the polar cap. Key Points: The large carbon‐dioxide ice deposits of Australe Mensa exhibit radar characteristics distinct from other low reflectivity zonesThe outlying low reflectivity zones show evidence of increased radar attenuation relative to layered regionsVariation in attenuation characteristics across low reflectivity zones suggests unique properties across units [ABSTRACT FROM AUTHOR]

Radar data can be of significant utility in investigating characteristics of rainfall events that cannot be studied with rain gauges alone. The recent establishment of a long‐term, quality‐controlled data set covering most of the radars on the Australian continent enables a deeper characterization of rainfall, including heavy rainfall events. This study develops a methodology to identify and characterize rainfall events from radar data and tests its utility by applying it to the regions surrounding the major cities of Brisbane, Sydney, and Melbourne. The event characteristics studied include rainfall accumulation and intensity, event duration and spatial extent, and the contribution convective areas make to the overall event rainfall. Rainfall events in Brisbane and Sydney are found to be more intense, more convective, and smaller in extent whilst producing larger rainfall accumulations than Melbourne rainfall events. Rainfall event duration and total accumulated rainfall are strongly positively correlated, as are the overall event intensity and the intensity of convective rainfall. The events that produce the largest rainfall accumulations exhibit significant differences from the events that produce the highest rainfall intensities. Overall, the study demonstrates that long‐term radar data sets in Australia provide an invaluable and rich source to study rainfall characteristics in a variety of regions at a high spatial and temporal resolution. Plain Language Summary: Weather radars produce a map of rainfall across a large area. Radars can observe characteristics of rain systems that are difficult to capture with rain gauge networks. A data set containing observations from weather radars in Australia can be used to study the characteristics of rain events. Such characteristics include how long an event lasts, how intense the rain is, how much rain falls overall, how large the event is, and how much of the rain comes from strong ascent (convection). This study develops a method of using radar to find rain events and assess their characteristics, and tests the method using Brisbane, Sydney, and Melbourne radar data. Rain events in Brisbane and Sydney have heavier rain and more rain coming from convective areas, and cover smaller areas, with more rain falling than in events in Melbourne. The longer a rain event is, the more rain is produced. An event having high rain intensity also means it produces intense rain from convective areas. Rain events that produce large rainfall amounts are distinct from those which have very intense rain. This study demonstrates that radar data sets in Australia are very useful for studying rain events. Key Points: Brisbane and Sydney rainfall events are more intense and convective than Melbourne rainfall events, which cover larger areasRainfall events with the highest accumulations and rainfall events with the highest intensities show significant differences in characterLong‐term radar data sets are useful for in‐depth analysis of rainfall and heavy rainfall event characteristics in Australia [ABSTRACT FROM AUTHOR]

This two‐part study presents a detailed analysis of (a) the vortex structure, (b) the inner core planetary boundary layer (PBL) wind profile, and (c) the overland surface winds of Typhoon Faxai (2019) during landfall observationally (Part I) and in high‐resolution Weather Research and Forecasting model simulations (Part II). Part I presents a comprehensive observational analysis of (a), (b), and (c) of Typhoon Faxai (2019) based on radar dual‐Doppler analysis and spatially dense surface wind observations over the Kanto region of Japan. An empirical model for the wind reduction factor F2 km$$ {F}_{2\;\mathrm{km}} $$ is proposed and validated with surface observations and radar velocity–azimuth display analyses at Narita and Haneda airports, both of which were affected by the passage of Faxai's inner core. Two distinct PBL jets associated with the inner rain band and the eyewall were observed over Narita Airport, which was located to the right of Faxai's track. Based on the empirical model of F2 km$$ {F}_{2\;\mathrm{km}} $$, it is found that the maximum surface wind speed observed at Narita Airport was caused by the peak of F2 km$$ {F}_{2\;\mathrm{km}} $$ due to a strong PBL jet associated with the eyewall located as close as ∼$$ \sim $$0.3 km from the ground. In addition, the wind swath analysis shows that a large area far inland on the right side of Faxai's track experienced strong surface 10‐min winds due to the combination of the passage of the eyewall PBL jet and the small surface roughness length around agricultural land such as paddy fields and farm fields. [ABSTRACT FROM AUTHOR]

The accurate prediction of high‐impact weather systems using cloud‐resolving models is still a challenge among researchers. This study evaluates the consistency of the combination of the three‐dimensional variational technique within the Gridpoint Statistical Interpolation assimilation system (GSI‐3DVAR) and nudging in the same modelling system on short‐range forecasts of three heavy rainfall events from the southwest monsoon season of 2021 over the Indian subcontinent. Three experiments have been conducted (i) control (CNTL): assimilated conventional and satellite observations; (ii) radar and lightning data assimilation (RLDA): assimilated radar reflectivity and lightning proxy reflectivity data along with all observations used in CNTL; and (iii) lightning data assimilation (LDA): same as RLDA but without the assimilation of radar data; particularly done to test the impact of assimilation of only lightning data. The model‐simulated rainfall is evaluated by using the Integrated Multi‐Satellite Retrievals (IMERG) for Global Precipitation Measurement (GPM IMERG) rainfall data. The intercomparison of LDA and RLDA for event 1 highlighted that both represent the convective regions reasonably better than CNTL, but RLDA outperforms LDA and thus further assimilation experiments are done with RLDA. RLDA provided reasonably accurate forecasts compared to CNTL, which is evident in the spatial distribution of rainfall and area‐averaged three‐hourly accumulated rainfall. Verification metrics for the three selected heavy rainfall events reveal that an optimal forecast performance (especially in the first six hours of free forecast) is obtained by the simulation with assimilation of radar and lightning data during the pre‐forecast period, through correcting the position and timing of convective centres. The probability of detection (POD) values are higher for light rainfall categories than for the heavy rain categories. POD values were higher in RLDA than CNTL throughout simulation for all three events. For all these three selected events, fractions skill scores (FSS) of RLDA are always better than CNTL with different neighbourhood sizes for different threshold values throughout the forecast period. [ABSTRACT FROM AUTHOR]

Precipitation microphysics in tropical cyclones (TCs) are often poorly represented in numerical simulations, which ultimately affects TC structure, evolution, and prediction. This provides a large incentive to better observe and understand the underlying microphysical processes in TCs in order to improve precipitation forecasts and warning operations. 112 TCs from 2014 to 2020 were matched up with overpasses from the NASA Global Precipitation Measurement (GPM) mission Dual‐Frequency Precipitation Radar (DPR) on a global scale to identify cloud properties and associated precipitation processes by quantifying vertical slopes of reflectivity in the liquid and ice phase. Further, vertical profiles of reflectivity were partitioned into different 850–200 hPa shear‐relative quadrants of each storm, different annuli from the storm center, and 5 TC ocean basins globally. Preliminary results showed the highest echo top heights occurred in the Northwest Pacific and Indian Ocean basins, with all basins and quadrants exhibiting median negative slopes of KuPR in the liquid phase. Additionally, all shear‐relative quadrants revealed negative slopes of reflectivity in the ice phase implying ice hydrometeor growth. These findings can potentially be used to improve the representation of cloud properties and the accuracy of the DPR particle size distribution algorithm in TCs. Plain Language Summary: Tropical cyclones play a key role in the Earth's water cycle by transferring heat and moisture from the tropics to the mid‐latitudes, which makes it important to better understand precipitation processes in these systems. As tropical cyclones spend a large portion of their life over the open ocean, the use of ground radar to monitor precipitation is rarely an option, which makes space‐borne radar a useful tool for quantifying precipitation. This study investigates precipitation processes in tropical cyclones on a global scale using space‐borne radar. The results suggest that precipitation mechanisms acting to increase or reduce the size of precipitation particles varied by ocean basin and in different areas relative to the storm center. These differences were determined based off the vertical profiles of radar reflectivity indicating a change in precipitation size and concentration with height. Key Points: A 7‐year global analysis of precipitation microphysics in tropical cyclones using space‐borne radarThere exists differences in microphysical processes in tropical cyclones by distance from the storm center, ocean basin, and shear‐relative quadrantAll ocean basins exhibit negative median slopes of reflectivity in both the liquid and ice phases, suggesting primarily hydrometeor growth [ABSTRACT FROM AUTHOR]

Mesoscale convective systems (MCSs) are crucial in modifying the water cycle and frequently induce high‐impact weather events over eastern China. Radar and Climate Prediction Center (CPC)‐4 km satellite‐derived infrared cloud top temperature (Tb) data were used to thoroughly analyze the long‐term climatology of MCSs over eastern China, particularly in the Yangtze–Huaihe River Basin (YHR) in the warm season from 2013 to 2018. For the first time, we contrasted the effects of data set selection and threshold setting on research outcomes. The large‐scale environments of MCSs initiation were also investigated using the latest global reanalysis data ERA5. It is found that striction of thresholds, including duration, reflectivity/Tb, area, and linearity, would lead to a greater proportion of early‐morning MCSs. Satellite‐identified MCSs differed from radar‐derived ones, exhibiting afternoon diurnal peaks, faster movement speeds, longer travel distances, and expansive impact areas. The center of MCS and related precipitation shifted northward from Pre‐Meiyu to Post‐Meiyu seasons, contributing to up to 20% of total rainfall, with most MCSs moving along eastward trajectories. MCSs typically had the most substantial impact in the Meiyu season because of the most prolonged duration, largest convective core area, and strongest precipitation intensity. Warm‐season MCSs initiated ahead of midlevel troughs and were related to strong anomalous low‐level convergence and midlevel upward. The circulation anomalies were the strongest in the Pre‐Meiyu season among the three subseasons, with most moisture sourced from the southwest. Plain Language Summary: This work used radar and satellite data to analyze the climate characteristics of a kind of large thunderstorms known as mesoscale convective systems (MCSs) in the Yangtze–Huaihe River Basin (YHR) in eastern China. These systems cover large areas, lasting from a few hours to a few days. The analysis revealed that stricter tracking thresholds would lead to more early‐morning MCSs. Satellite‐identified MCSs differed from those identified by radar. They tended to have more thunderstorms in the afternoon, move faster, travel longer distances, and cover larger areas. MCSs also contributed to up to 20% of the total warm‐season rainfall in the YHR. MCSs typically had the most substantial impact in the rainy season of eastern China from mid‐June to mid‐July. Warm‐season MCSs initiated ahead of low‐pressure systems and were related to strong anomalous low‐level convergence and midlevel upward motion. During the time before the rainy season, the large‐scale forcing needed to be anomaly enough to trigger MCS formation. Most of the moisture that caused MCSs came from southwest direction instead of stemming locally during this period. Key Points: Six‐year climatology of warm‐season mesoscale convective systems (MCSs) in Yangtze–Huaihe River Basin is investigated using satellite, radar data, and ERA5Criteria setting for tracking MCSs noticeably influences the derived featuresMeiyu season MCSs featured longest duration, largest areas, and heaviest precipitation, while the background circulation anomalies are the weakest [ABSTRACT FROM AUTHOR]

Multipath interference in radar signals caused by sea, ground, and other environments poses significant challenges to the target detection, tracking, and classification capabilities of radar systems. Existing methods for radar signal identification require labeled samples and focus mainly on the classification of normal signals. However, in practice, anomalous samples (multipath interference signals) may be scarce and highly imbalanced (i.e., mostly normal samples). To address this problem, we propose a deep anomaly detection with attention (DADA) for semisupervised detection of multipath radar signals. The method transforms radar signals into time–frequency images and is trained exclusively on normal samples. The autoencoder architecture is extended with a feature extractor network to capture latent sample features. CBAM attention is introduced to improve feature extraction. By learning the distribution of normal samples in high-dimensional image space and low-dimensional feature space, a two-dimensional feature space representing normal samples is constructed. A one-class SVM then learns the boundary of normal samples for anomaly detection. Extensive experiments on radar signal datasets validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Radar profiling algorithms are widely used for precipitation‐induced path‐attenuation correction. In this study, we investigate the impact of the four well‐known profiling algorithms on the efficacy of quantitative precipitation estimation retrievals. The first method, named the "simple PhiDP‐based method", distributes precipitation‐induced path attenuation, or PhiDP (differential phase shift), of a rain segment to each radar gate according to radar reflectivity (Zh). The second one is a linear version of the ZPHI method derived from an approximate linear relationship between specific attenuation (Ah) and specific differential phase (KDP). The third one is a nonlinear version of the ZPHI that respects the nonlinearity of the Ah–KDP relationship. The fourth method is inspired from the self‐consistent method of Bringi. The two‐moment normalized raindrop size distribution (DSD) with two variables, scaled drop concentration Nw$$ {N}_{\mathrm{w}} $$ and mean drop diameter Dm$$ {D}_{\mathrm{m}} $$, is used to represent natural variability of DSD. Two configurations are considered: a constant Nw$$ {N}_{\mathrm{w}} $$ over a segment and a variable Nw$$ {N}_{\mathrm{w}} $$ over a segment. The Ah, KDP, and rain intensity R$$ R $$ are estimated by the four profiling algorithms. The sensitivity of each algorithm to radar measurements uncertainty is also investigated. The evaluation of the estimates shows the following. First, for the range‐constant Nw$$ {N}_{\mathrm{w}} $$ situation, the nonlinear ZPHI and the self‐consistent method yield the best estimates if the measurements errors are not considered. However, the nonlinear ZPHI and the self‐consistent method are significantly impacted by the Zh bias and the measurements noises respectively. Second, for the range‐variable Nw$$ {N}_{\mathrm{w}} $$ situation, the self‐consistent method does not have a good performance even when radar measurements are accurate. The performance of the nonlinear ZPHI method is similar to the simple PhiDP‐based method, whereas the latter is immune to the Zh calibration error. These results suggest that the benefit of introducing Nw$$ {N}_{\mathrm{w}} $$ in quantitative precipitation estimation algorithms can be significantly reduced if Nw$$ {N}_{\mathrm{w}} $$ is not constant in reality and/or if the radar measurements are biased and noisy. [ABSTRACT FROM AUTHOR]

A fundamental challenge in the practical implementation of multistatic radar systems (MSRS) is the requirement for precise time and frequency synchronisation between the spatially separated radar nodes. The authors evaluate the performance of different classes of commercially available Global Navigation Satellite Systems (GNSS) timing receivers, Local Oscillators (LO) and GNSS Disciplined Oscillators (GNSSDOs) to determine the limitations of using one‐way GNSS Time and Frequency Transfer (TFT) in this application. From evaluating the performance of three pairs of GNSSDOs, it is concluded that one‐way GNSS TFT will likely be suitable only for the synchronisation of fully spatially coherent MSRS with carrier frequencies up to 100 MHz and waveform bandwidths up to 20 MHz. Whereas, in the case of short‐term spatially coherent MSRS, synchronisation of systems with carrier frequencies up to a few GHz and waveform bandwidths of over 100 MHz will likely be possible. The performance of the different classes of GNSSDOs during GNSS denial (holdover) are evaluated, where it is concluded that frequency offsets between LOs at the point of GNSS denial will often significantly contribute, or even dominate, the holdover performance. Analysis of two practical multistatic radar measurements verifies the function of using the GNSSDOs for wireless synchronisation of the ARESTOR MSRS. [ABSTRACT FROM AUTHOR]

This article is a guest editorial for the IET Radar, Sonar & Navigation journal, focusing on selected papers from the RADAR 2022 conference held in Edinburgh, UK. The conference provided an opportunity for radar specialists from 22 countries to explore the latest developments in radar systems. Key topics discussed at the conference included new radar trends, target detection (with a focus on drones), low-frequency radar, and cognitive radar. The special issue contains 17 papers based on extended work presented at the conference, covering topics such as multistatic radar, passive radar, target signatures, and advanced radar processing techniques. The authors hope that this special issue will serve as a valuable resource for further research in the field. [Extracted from the article]

The presence and importance of small drones (unmanned aerial vehicles) are increasing. With traditional radar detection the target radar cross section and its statistical properties are needed in order to design the detector and to assess detection performance. The authors make a statistical analysis of the radar cross section of two small fixed‐wing drones using measured data from typical flights. Using data from a typical flight instead of measurements or electromagnetic calculations as a function of aspect angle is a convenient method. Work results of the authors in mean values, medians and standard deviations, in analyses of common target probability distributions and in decorrelation times for the radar cross section, are necessary for design and assessment of the detection performance. The authors suggest a new target probability distribution which agrees better with the measured data than the others tested. The authors also note significant differences between the drones. [ABSTRACT FROM AUTHOR]

An analysis of the parameter estimation uncertainty for the target location and velocity achievable using a single‐transmitter‐multiple‐receiver multistatic radar system is presented. A framework for establishing measures of multistatic radar parameter uncertainties by expansion of the bistatic radar parameter uncertainty measures is presented for systems containing omnidirectionally radiating nodes. The methodology uses analytical methods based on the Cramér–Rao Lower Bounds applied to scenarios in a two‐dimensional physical space with a single target exhibiting Doppler characteristics and a bistatic angle dependent radar cross‐section. A set of geometric descriptors is proposed to characterise the system, and parameter uncertainty results are reported as a function of these descriptors. The results indicate that angular separation between the transmitter and the centre of the receiver distribution is of greater importance than the quantity of receivers within the system when low uncertainty estimation capabilities are desired, though a minimum of two receivers must be available. The proportion of receivers within the system which contributed information crucial to obtaining the minimum estimation uncertainty is reported for systems containing different quantities of receivers. It was observed that, as the total number of receivers available increased, the proportion of receivers required to achieve the minimal uncertainty level reduces significantly. [ABSTRACT FROM AUTHOR]

The use of WiFi signals for sensing purposes has attracted a lot of interest from both the radar and communications communities and several techniques have been explored. In the attempt of meeting the requirements for small sensor size, compactness, and easy deployment, the authors consider reference‐free approaches, namely approaches that do not require a good copy of the transmitted waveform to be available at the radar receiver. To this end, the authors first resort to a passive radar‐based processing scheme that only exploits the invariant a priori known initial portion of the physical layer protocol data unit, that is, the PHY Preamble, and its limitations in practical applications is investigated. Specifically, the authors show that, with this approach, an accurate time, phase, and frequency synchronization is essential and a possible strategy is investigated. As an alternative solution the authors consider a forward scatter radar‐based approach where only the amplitude modulation of the received signal is exploited to detect the presence of a moving target thus avoiding the need to know the transmitted signal. For the first time, the authors comparatively investigate advantages and drawbacks of the two reference‐free approaches and present practical strategies to handle the limitations observed. The results are reported for experimental tests with people and drones using WiFi transmissions in the 2.4 and 5 GHz band. [ABSTRACT FROM AUTHOR]

With the advent of constellations of SAR satellites, and the possibility of swarms of SAR UAV's, there is increased interest in multistatic SAR image formation. This may provide advantages including allowing three‐dimensional image formation free of clutter overlay; the coherent combination of bistatic SAR geometries for improved image resolution; and the collection of additional scattering information, including polarimetric. The polarimetric collection may provide useful target information, such as its orientation, polarisability, or number of interactions with the radar signal; distributed receivers would be more likely to capture any bright specular responses from targets in the scene, making target outlines distinct. Highlight results from multistatic polarimetric SAR experiments at the Cranfield University GBSAR laboratory are presented, illustrating the utility of the approach for fully sampled 3D SAR image formation, and for sparse aperture SAR 3D point‐cloud generation with a newly developed volumetric multistatic interferometry algorithm. [ABSTRACT FROM AUTHOR]

The marginal Fisher information (MFI) metric is used to design waveforms for the sake of informationally optimal adaptive‐on‐transmit radar operation. A framework for MFI waveform design is developed and the Polyphase‐Coded FM (PCFM) waveform model is utilised to produce a constant‐modulus, spectrally contained signal amenable to transmission with high‐power amplifiers. The efficacy of the MFI waveform design and minimum mean square error (MMSE) estimation is experimentally demonstrated and extended into an adaptive and dynamic sensing paradigm. The radar transmit waveform is optimised to maximise the Fisher information with respect to the range profile. Upon observing new information from radar echoes, the iterative MMSE (iMMSE) estimator then minimises the estimation error variance according to prior observations. Sequential information maximisation (via waveform design) and error minimisation (via iMMSE) tends towards the Cramér–Rao lower bound (CRLB) with additional measurements improving radar resolution and accuracy. These concepts maximise the information extracted by a radar operating in a congested spectrum where the available bandwidth is limited. [ABSTRACT FROM AUTHOR]

Urban surveillance of slow‐moving small targets such as drones and birds in low to medium airspace using radar presents significant challenges. Detecting, locating and identifying such low observable targets in strong clutter requires both innovation in radar hardware design and optimisation of processing algorithms. To this end, the University of Birmingham (UoB) has set‐up a testbed of two L‐band staring radars to support performance benchmarking using datasets of target and clutter from realistic urban environment. This testbed is also providing the vehicle to understand how novel radar architectures can enhance radar capabilities. Some of the challenges in installing the radar at the UoB campus are highligted. Detailed benchmarking results are provided from urban monostatic and bistatic field trials that form the basis for performance comparison against future hardware modification. The solution to the challenge of interfacing the radar to the external oscillators is described and stand‐alone bench tests with the candidate oscillators are reported. The testbed provides a valuable capability to undertake detailed analysis of performance of Quantum photonic‐enabled radar and allows for its comparison with conventional oscillator technology for surveillance of low observable targets in the presence of urban clutter. [ABSTRACT FROM AUTHOR]

In this article, a simple, yet effective, Bayesian scheme for tracks maintenance, promotion, and deletion in drone surveillance radar is presented. It enables the simultaneous tracking of the target body and micro‐Doppler components that originate from the motion of rotors (if any) onboard an unmanned air system. This not only delivers more accurate multi‐target tracking, but also substantially improves the radar automatic target classification capability (e.g. discriminating between drone and non‐drone targets). Challenging and diverse real staring radar datasets are used here to demonstrate the efficacy and benefits of the proposed track management approach. [ABSTRACT FROM AUTHOR]

The millimetre wave radar signatures of sea lions collected from three animals in the outdoor seal pool available at the Sea Mammal Research Unit in St Andrews in the Autumn of 2021 is reported. The objective is to study the radar amplitude and Doppler signatures of the animals when their full body or part thereof is above water, which is important for the application of autonomous marine navigation. The data was collected using 24 GHz (K‐band) and 77 GHz (W‐band) Frequency Modulated Continuous Wave radars with linear polarisation. It has been demonstrated that the sea lions were very clearly detected by the radars with Signal to Noise Ratio greater than 30 dB at a range of 40 m. The calculated modal radar cross section (RCS) of the sea lions in HH polarisation at 24 and 77 GHz vary from −48 to −26 dBsm and −48 to −28 dBsm respectively, corresponding to the different body parts and the amount of exposure to the radar beam. In VV polarisation, the modal RCS value range is from −49 to −26 dBsm and −49 to −22 dBsm respectively. The corresponding maximum RCS and the Cumulative Distribution Function results are also reported. [ABSTRACT FROM AUTHOR]

The paper focuses on the results of experimental research on the possibility of passive space object detection using a single radio telescope from the European Low‐Frequency Array (LOFAR) network of astronomical radio telescopes. Commercial digital television (DVB‐T) transmitters were used as illuminators of opportunity in this radar system. In the conducted experiments, one LOFAR radio telescope served both as a surveillance receiver and reference receiver in a passive radar system. The greater part of the LOFAR telescope array was used to observe a space object, while a small part of the array was directed towards the illuminator of opportunity to record the reference signal. One of the most important problems to overcome with utilising the LOFAR radio telescope in such a solution was the effective suppression of the direct‐path component in a surveillance signal coming from the illuminator of opportunity, which was relatively close to the LOFAR radio telescope. The results regarding the passive detection of the International Space Station, included in the paper, confirm the possibility of observing the space object flying in Low‐Earth Orbit using the LOFAR telescope or another receiving system with a similar antenna array. [ABSTRACT FROM AUTHOR]

This paper proposes a simple non‐adaptive approach for the suppression of direct signal and clutter contributions in a passive radar system based on orthogonal frequency division multiplexing transmissions, exploiting the properties of a reciprocal filter (RF). The use of a RF allows to circumvent the limits posed by the waveform ambiguity function, producing a data‐independent time‐invariant response to a stationary point‐like target echo at the output of the range compression stage. This feature enables simple clutter cancellation strategies, based on the subtraction of delayed portions of the surveillance signal, according to the conventional moving target indication methodologies. The proposed processing scheme consists of a range compression stage based on a RF, followed by a Single Canceller. Its performance is investigated against a simulated and an experimental data set, for the case of a digital video broadcasting‐terrestial‐based passive radar scenario, and it is compared to an alternative scheme based on a conventional matched filter and adaptive cancellation techniques. The proposed approach is shown to yield a perfect cancellation of the clutter returns from a stationary scene under ideal conditions. Moreover, it proves to be robust to real‐world conditions, despite its considerably limited computational load compared to adaptive clutter cancellation techniques. [ABSTRACT FROM AUTHOR]

Ecosystem functions and services are severely threatened by unprecedented global loss in biodiversity. To counteract these trends, it is essential to develop systems to monitor changes in biodiversity for planning, evaluating, and implementing conservation and mitigation actions. However, the implementation of monitoring systems suffers from a trade‐off between grain (i.e., the level of detail), extent (i.e., the number of study sites), and temporal repetition. Here, we present an applied and realized networked sensor system for integrated biodiversity monitoring in the Nature 4.0 project as a solution to these challenges, which considers plants and animals not only as targets of investigation, but also as parts of the modular sensor network by carrying sensors. Our networked sensor system consists of three main closely interlinked components with a modular structure: sensors, data transmission, and data storage, which are integrated into pipelines for automated biodiversity monitoring. We present our own real‐world examples of applications, share our experiences in operating them, and provide our collected open data. Our flexible, low‐cost, and open‐source solutions can be applied for monitoring individual and multiple terrestrial plants and animals as well as their interactions. Ultimately, our system can also be applied to area‐wide ecosystem mapping tasks, thereby providing an exemplary cost‐efficient and powerful solution for biodiversity monitoring. Building upon our experiences in the Nature 4.0 project, we identified ten key challenges that need to be addressed to better understand and counteract the ongoing loss of biodiversity using networked sensor systems. To tackle these challenges, interdisciplinary collaboration, additional research, and practical solutions are necessary to enhance the capability and applicability of networked sensor systems for researchers and practitioners, ultimately further helping to ensure the sustainable management of ecosystems and the provision of ecosystem services. [ABSTRACT FROM AUTHOR]

This paper presents experimental verification results for a finite‐sized array of a simple non‐reciprocal metasurface elements. The metasurface elements consists of ferrite loaded metal patches. Experimental results demonstrate nonreciprocity, achieving isolation exceeding 20 dB at 6.4 GHz in a 2×2 array. Additionally, these findings are compared with simulation results for a 4×4 array to validate its practicality in a finite array setting. [ABSTRACT FROM AUTHOR]

Although radar sensors have been known for their robustness in challenging environmental conditions, they face difficulties in distinguishing between upper structure (US), such as road signs, traffic lights, overpasses, and pedestrian bridges, and forward vehicle (FV). This misclassification can lead to false alarms and unwanted braking events. In this paper, the authors propose a method distinguishing between US and FV at different distances based on change in radar cross section (RCS). The study collects real‐world RCS data from various objects identified as US and FV during road driving scenarios. [ABSTRACT FROM AUTHOR]

This article discusses the importance of radar systems and processing methods for space situational awareness (SSA). It highlights the vulnerability of the space environment due to the increasing number of satellites and space debris, as well as the growing threat of attacks. Radar sensors are particularly useful for SSA because they can detect small debris over long ranges and accurately track objects. The article presents various topics related to radar systems for SSA, including ground-based and space-based radar, multistatic radar networks, and cognitive radar and deep-learning methods. It concludes by providing an overview of the articles included in the special issue. [Extracted from the article]

During 6–7 May 2017, a record‐breaking nocturnal rainfall event occurred in Guangzhou, China, and it was a typical warm‐sector heavy rainfall event under weak synoptic forcing. A prior observational study by the authors revealed that warm‐rain microphysical processes were dominant and responsible for the record‐breaking precipitation. In this study, the double‐moment Morrison, Thompson and NSSL microphysics schemes in WRF are evaluated against polarimetric radar observations in their ability of reproducing observed microphysical characteristics. The Thompson scheme shows the greatest fidelity to the observed raindrop size distribution (RSD) median value, corresponding to the most amount of precipitation forecast. While the Morrison and NSSL simulations overestimate (underestimate) the raindrop size (number concentration), exhibiting continental‐type convective precipitation. The three experiments slightly overestimate differential reflectivity (ZDR), but significantly underestimate specific differential phase (KDP) and liquid water content by about 30%–50%, implying the undervaluation of number of medium‐sized raindrops. Examinations of the occurrence frequencies of ZDR, KDP, mass‐weighted diameter, and logarithmic normalized intercept parameter for rain suggest that all three schemes fail to reproduce the full variability of observed RSD for the extreme rainfall. The vertical variations of RSD parameters and the Kumjian‐Ryzhkov parameter space suggest that the collision–coalescence is the dominant warm‐rain microphysical process but the simulated process is too weak. This may be attributed to the misrepresented RSD near the melting layer, where the raindrops with lower number concentration and larger sizes cannot grow through the collision–coalescence process as actively. Plain Language Summary: Extreme rainfall is of great concern due to its potential to cause major disasters. Cloud microphysics has been considered one of the main sources of uncertainty in predicting rainfall at cloud‐resolving resolutions. This study investigates the performances of three commonly used schemes representing cloud precipitation processes in an extreme rainfall case that occurred in the warm and moist environment of South China. The Thompson scheme is found to perform better in simulating raindrop sizes and surface precipitation. None of the three schemes reproduces the full variation of observed raindrops, and all of them underestimate raindrop size growth through collision processes. These results suggest areas needing improvement in microphysics schemes for predicting extreme precipitation in sub‐tropical regions. Key Points: Thompson scheme better reproduces raindrop median size and number concentration than two other schemes in a Guangzhou extreme rain eventNone of the three schemes examined adequately predicts the full variability of raindrop size distribution important for extreme rainfallLow raindrop concentration and excessively large sizes in simulations jointly cause the too weak collision–coalescence processes [ABSTRACT FROM AUTHOR]

This study used a Weather Research and Forecasting (WRF)‐based Ensemble Kalman Filter (EnKF) system to assimilate reflectivity (Z) and radial velocity (Vr) data in precipitating and clear‐air regions from the Beijing Daxing International Airport C‐band phased‐array radar (C‐PAR) to improve the forecasts of a convective initiation (CI) case occurred on 18 June 2020. The results showed that high‐frequency assimilating the C‐PAR Vr in clear‐air region is conducive to increase the forecast lead time of CI by significantly improving the initial dynamic and thermodynamic fields, which creates a more accurate pre‐CI environment. After assimilating the C‐PAR clear‐air Vr, the CI case can be accurately predicted with a 20 min forecast lead time in the best‐case scenario. This is the first real‐case study to demonstrate the benefits of assimilating high spatiotemporal resolution PAR clear‐air radial velocity data for the CI process. Plain Language Summary: Convection initiation is the beginning stage of severe convective weather. Accurate prediction of its location and timing is very important for monitoring and early warning of severe convective weather. However, CI forecast is still a great challenge worldwide. The good news is that newly deployed phased‐array radar (PAR) with high spatiotemporal resolution can capture the much needed small‐scale information of the preconvective environment and the whole CI process. This study combines the new PAR observation and numerical weather prediction model through Ensemble Kalman Filter (EnKF), one of the most popular data assimilation techniques, to improve forecast of a CI event. With the improved data, we successfully forecast CI process mainly thanks to the PAR that help improved three‐dimensional winds, temperature, and humidity analysis, particularly in the clear‐air regions. Key Points: First real‐case assimilation of C‐band phased‐array radar clear‐air data on convection initiation using Ensemble Kalman FilterAssimilating clear‐air radial velocity plays a key role in successful forecast of convective initiation (CI)Assimilating clear‐air radial velocity has the potential to accurately forecast CI with a lead time up to 20 min [ABSTRACT FROM AUTHOR]

An extensive investigation of the interaction of thermoplastic olefin (TPO) compounds with high frequency radar wave has been conducted to understand how TPO materials affect radar sensor efficiency, an important function for vehicle safety and especially the advancement of autonomous vehicles. In this article, the effect of major ingredients of a TPO on its dielectric properties was studied, and then a wide range of TPO compounds were evaluated for their radar transmittance or attenuation. It was demonstrated that the characteristics of polypropylene (PP) resin and rubber have little impact on the dielectric properties of a TPO, thus minimal influence on TPO's radar transparency, while increasing the talc loading in the compound increases the dielectric constant of the TPO. The TPO materials containing talc from 7% up to 34% were found to have dielectric constants ranging from 2.30 to 2.70 and nearly zero dielectric loss. Experiments confirmed that parts molded with these materials are transparent to high frequency radar wave at optimized thicknesses. Some pigments for coloring the materials also affect dielectric properties of TPOs, thus radar transmittance, with metal pigments showing the most impact. Finally, commercial TPO products are shown to have great batch to batch consistency in radar transmittance at a selected part thickness. [ABSTRACT FROM AUTHOR]

As theoretical research further develops, coding metasurfaces have been widely studied and applied due to their uniqueness in digital characterization and sequence arrangement. In this work, based on highly conductive graphene‐assembled film (GAF), an ultralight anisotropic broadband coding metasurface (ABCM) is proposed. The GAF ABCM consists of 16 units with specific phase responses and enables complete metal substitution. By arranging the unit cells in a specific order, the GAF ABCM realizes the functions of anomalous reflection, polarization conversion, and radar cross‐section (RCS) reduction. Results validate that the GAF ABCM has a measured 10 dB normal incidence backward RCS reduction within the working frequency band of 7–18 GHz, which fully covers the X‐band and Ku‐band for radar application. Furthermore, due to the lightweight property of GAF, the density of the GAF ABCM is only 0.06 g/cm3 (surface mass density of 0.038 g/cm2). All results indicate that the lightweight GAF metasurface is promising in radar stealth and communication fields. [ABSTRACT FROM AUTHOR]

Due to the controllable degrees of freedom of frequency diverse array in the distance dimension, the frequency diverse multiple‐input multiple‐output (FDA‐MIMO) radar can easily deal with mainlobe interference, which is often difficult to solve in traditional phased arrays. Nevertheless, the performance of FDA‐MIMO radar will suffer from degradation in the scenario where spectrum interferences and signal‐dependent interferences coexist. In order to solve this problem, a novel FDA‐MIMO radar framework via antenna switching is proposed. Based on this framework, an effective method for jointly optimising antenna selection and beamforming against spectrum interferences and signal‐dependent interferences is developed. The resulting optimization problem is nonconvex and NP‐hard owing to the integer constraints caused by antenna selection. By relaxing the integer constraints, the original problem can be transformed into a convex optimization form and an iterative reweighting strategy is used to force the obtained solution to satisfy the integer constraints. Simulation examples show that the proposed algorithm has better performance in output signal‐to‐interference‐plus‐noise ratio (SINR) and beampattern radiation performance than the existing competitive methods. [ABSTRACT FROM AUTHOR]

A great interest has been directed towards high frequency surface wave radar (HFSWR), because it is applied as long‐range early warning and real‐time measurements of sea surface condition tools in maritime surveillance. However, the unwanted reflection from the ionosphere (ionosphere clutter) damages the consistent detection performance of HFSWR. Target detection and environmental information extraction from radar echoes are primarily determined by the extent to which the medium contaminates the transmitted signals. Although the ionospheric signal corruption mechanisms are complex and various, a number of eliminating contamination approaches have been developed in the last 50 years. The purpose of this paper is providing an overview of the past and current clutter theoretical models and clutter suppression techniques for HFSWR system. The experimental results involved in the complex long‐term ionosphere observation and ionosphere echo analysis are first discussed. Special attention is paid to the correlation between the signal degradation sources and eliminating clutter techniques. Furthermore, the clutter suppression methods are classified based on the design idea. The specific clutter suppression methods as well as its operation principle are briefly introduced. Finally, several research trends and open issues are presented, with emphasis on the need for proactively controlling the radar design cost and generally agreed‐upon standards. [ABSTRACT FROM AUTHOR]

Active deception jamming recognition has gained significant attention as a crucial aspect of modern electronic warfare, and a large quantity of jamming recognition methods based on either artificial or deep learning methods have been proposed to date. In actual complex battlefields, the abundant deceptive jamming signals are extremely difficult to obtain and the deceptive jamming signals leveraged by the adversarial jammers are often significantly influenced by noise. To address these challenges a Siamese squeeze wavelet attention network (SSWAN) for radar active deception jamming recognition method is proposed. By constructing a wavelet attention module (WAM), the fine‐grained time‐frequency texture features of echo and jamming signals are preferably extracted even under prohibitively strong noise scenarios. Specifically, the Siamese structure is employed as the main backbone network to measure the similarity between jamming signals and make full use of the training samples; besides, the squeeze learning module is embedded to maintain lightweight and prevent overfitting. Experimental results demonstrate that at a jamming‐to‐noise ratio (JNR) of −8 dB, the proposed method achieves recognition accuracies above 96.3% for 6‐class and multi‐class combinational active deceptive jamming types. Overall, compared to mainstream deep networks, the proposed method exhibits superior advantages in lower JNR ratio and small samples of actual battlefield scenarios. [ABSTRACT FROM AUTHOR]

Interrupted sampling repeater jamming can produce numerous active false targets with high fidelity, which is a significant challenge for coherent pulse radar. This poses a threat to the effectiveness of anti‐jamming technology, such as time‐frequency analysis and frequency agility for conventional constant pulse repetition interval (PRI) radar, which will render the radar inoperable to detect and track the actual target. A random PRI and intra‐pulse frequency agile radar waveform with low interception properties is proposed, which can effectively restrict the acquisition of radar waveform parameters by the jammer, destroy the coherence of jamming signals, and capture the range‐dimensional centroid feature of the echo. Accordingly, a jamming identification method based on the scale constrained Gaussian mixture model and an entity radar target energe recovery algorithm based on the neighbourhood weighted maximum entropy are proposed for target detection. Theoretical analysis and simulation experiments demonstrate the effectiveness of the proposal. [ABSTRACT FROM AUTHOR]

The transmit waveform with ambiguity function specifications in the range‐Doppler plane has been of great interest in traditional single antenna radar systems in recent years. This paper considers the design of waveform sets with a desired slow‐time ambiguity function for multiple‐input multiple‐output radar. Specifically, it is desirable that the ambiguity function has low sidelobes where clutter occurs, as this reduces the impact of the clutter on target detection. Instead of unimodular waveforms, the authors consider amplitude modulation signals and introduce the bound constraint on signal amplitudes into the optimisation for the consideration of transmitter efficiency. The design problem is formulated as minimising the weighted integrated sidelobes level of the ambiguity function over the range‐Doppler plane under bound constraints. The resulting problem has a highly non‐linear objective function and is constrained by bound constraints and quadratic equality (constant energy) constraints, making it difficult to solve. The authors develop an efficient algorithm based on the sequential quadratic programming technique and non‐linear conjugate gradient method to solve it. Simulation results show that the proposed algorithm is superior to the existing methods. [ABSTRACT FROM AUTHOR]

Short‐wave source localisation has an important role in many fields. A two‐stage iterative algorithm is proposed based on the ionospheric virtual height (IVH) model using the direction of arrival (DOA) and time difference of arrival (TDOA) measurements, which is divided into DOA stage and TDOA stage. The optimisation problem of the TDOA stage can be obtained by using the result of DOA stage and TDOA measurements. During the solution process, the non‐convex quadratic equation constraints are converted to linear constraints, which can lead to the algorithm converges to the global optimal solution and the optimal solution can be analytically solved. The theoretical analysis demonstrates that the localisation accuracy of both stages can reach the corresponding constrained Cramér‐Rao lower bound (CCRLB). And the accuracy of TDOA stage achieves the CCRLB of hybrid localisation. Finally, the performance of the proposed method is demonstrated by simulations. [ABSTRACT FROM AUTHOR]

Kurzfassung: Mit dem Begriff der Forensik wird meist ein Fachgebiet der Medizinwissenschaften verbunden. Die Forensik bezeichnet jedoch viel mehr ein wissenschaftliches und technisches Arbeitsgebiet, in dem kriminelle Handlungen systematisch untersucht werden. An Bauwerken ist es, zum Glück, meist nicht erforderlich kriminelle Handlungen zu untersuchen, sehr wohl aber Schäden, Mängel und unbekannte Zustände. In diesem Kontext leistet man mit herkömmlichen und modernen Untersuchungsmethoden der Bauwerksdiagnostik durchaus forensische Arbeit. Grundlage für die Ermittlung der tatsächlichen Schadensursachen ist eine systematische Untersuchung der Konstruktion, der Materialien und Zustände am Bauwerk. Diese beginnt mit einer Sichtung der Bestandspläne und visuellen Inspektion der Gesamtkonstruktion. Aus den daraus abgeleiteten Schadenshypothesen wird eine Untersuchungsplanung aufgebaut. Auf deren Basis wird das Bauwerk zunächst zielgerichtet und systematisch mit zerstörungsfreien Prüfverfahren (ZfP) untersucht, gefolgt von minimalinvasiven zerstörungsarmen Untersuchungen zur Verifizierung und Kalibrierung der ZfP‐Daten. Anhand von aktuellen Beispielen werden der Ablauf und die Bedeutung derart systematischer Untersuchungen unter Zuhilfenahme von ZfP‐Verfahren verdeutlicht. [ABSTRACT FROM AUTHOR]

Ionospheric convection patterns from the Super Dual Auroral Radar Network are used to determine the trajectories, transit times, and decay rates of three polar cap patches from their creation in the dayside polar cap ionosphere to their end of life on the nightside. The first two polar cap patches were created within 12 min of each other and traveled through the dayside convection throat, before entering the nightside auroral oval after 104 and 92 min, respectively. When the patches approached the nightside auroral oval, an intensification in the poleward auroral boundary occurred close to their exit point, followed by a decrease in the transit velocity. The last patch (patch 3) decayed completely within the polar cap and had a lifetime of only 78 min. After a change in drift direction, patch 3 had a radar backscatter power half‐life of 4.23 min, which reduced to 1.80 min after a stagnation, indicating a variable decay rate. 28 minutes after the change in direction, and 16 min after coming to a halt within the Clyde River radar field‐of‐view, patch 3 appeared to reach its end of life. We relate this rapid decay to increased frictional heating, which speeds up the recombination rate. Therefore, we suggest that the slowed patch motion within the polar cap convection pattern is a major factor in determining whether the patch survives as a recognizable density enhancement by the time the flux tubes comprising the initial patch cross into the nightside auroral oval. Key Points: Tracking of high‐density plasma volumes in the ionosphere is a viable tool for uniting spatially distant observationsA drifting polar cap patch has variable plasma decay rate at different stages of its lifetimeStagnation of a polar cap patch is considered a major determinant for a complete decay [ABSTRACT FROM AUTHOR]

Various types of radar systems are increasingly being used to monitor aerial biodiversity. Each of these types has different detection capabilities and sensitivities to environmental conditions, which affect the quantity and quality of the measured objects of interest. Radar wind profilers have long been known to detect birds, but their use in ornithology has remained limited, largely because of biologists' unfamiliarity with these systems. Although the potential of radar wind profilers for quantitative bird monitoring has been illustrated with time series of raw data, a comparison with a similar radar system more established in biology is missing. Here, we compare nocturnal bird migration patterns observed by a radar wind profiler during October 2019 and April 2021 with those from a dedicated bird radar BirdScan MR1. The systems were located 50 km apart with an altitudinal difference of about 850 m. The nightly migration intensities measured with both systems were highly correlated in both spring and autumn (Pearson correlation coefficient ≈ 0.8, P < 0.001), but estimated traffic measured by the radar wind profiler was on average five times higher in spring and nine times higher in autumn. Low ratios of the migration traffic rates of the Birdscan MR1 to those of the radar wind profiler occurred primarily in clear conditions. In both radar systems, migration occurred at significantly higher altitudes in spring than in autumn. Discrepancies in absolute numbers between both systems are likely due to both system‐inherent and external environmental and topographical factors, but also different quantification approaches. These findings support the capacity of radar wind profilers for aerial biomonitoring, independent of environmental conditions, and open up further avenues for studying the impact of weather on bird migration at detailed temporal and altitudinal scales. [ABSTRACT FROM AUTHOR]

Measuring the radar backscatter characteristics of glacier ice at different frequencies and incidence angles is fundamental to predicting the glacier mapping performance of a sensor. However, such measurements at 94 GHz do not exist. To address this knowledge gap, we collected 94 GHz radar backscatter data from the surface of Rhônegletscher in Switzerland using the All‐Weather Volcano Topography Imaging Sensor (AVTIS2) real‐aperture Frequency Modulated Continuous Wave radar. We determine the mean normalized radar cross section σmean0 $\left({\sigma }_{\text{mean}}^{0}\right)$ to be −9.9 dB. The distribution closely follows a log‐normal distribution with a high goodness of fit (R2 = 0.99) which suggests that radar backscatter is diffuse and driven by surface roughness. Further, we quantified the uncertainty of AVTIS2 3D point clouds to be 1.30–3.72 m, which is smaller than other ground‐based glacier surface mapping radars. These results demonstrate that glacier surfaces are an efficient scattering target at 94 GHz, hence demonstrating the suitability of millimeter‐wave radar for glacier monitoring. Plain Language Summary: Radar sensors map glacier surfaces by transmitting a signal at a specific frequency and measuring its return strength when reflected back. This returned signal strength, called radar backscatter, is determined by the characteristics of the glacier surface and varies with radar frequency and sensor viewing angle. Millimeter‐wave radars operating at 94 GHz can acquire high resolution measurements of glaciers in most weather conditions. However, there are currently no measurements of radar backscatter from glacier surfaces at this frequency. We therefore acquired the first ever measurements of 94 GHz radar backscatter from glacier ice. The results are consistent with those expected from randomly rough surfaces, hence we conclude that the roughness of the glacier surface is the primary driver of 94 GHz radar backscatter. We also show that 3D glacier surface mapping at this frequency is more accurate than other ground‐based radars that are employed to map glacier geometries. The results overall indicate that 94 GHz radar is an effective tool for glacier monitoring and thus opens up new possibilities for studying glacier processes. Key Points: 94 GHz radar backscatter from alpine glacier ice has been characterized for the first timeSurface roughness is the primary factor in 94 GHz radar backscatter from glacier iceThe uncertainty of 3D glacier mapping using 94 GHz radar has been quantified [ABSTRACT FROM AUTHOR]

Stealth technology is used to enhance the survival of military equipment in the field of military surveillance, as it utilizes a combination of techniques to render itself undetectable by enemy radar systems. Radar absorbing materials (RAMs) are specialized materials used to reduce the reflection (or absorption) of radar signals to provide stealth capability, which is a core component of passive countermeasures in military applications. The properties of RAMs can be optimized by adjusting their composition, microstructure, and surface geometry. Carbon‐based materials present a promising approach for the fabrication of ultrathin, versatile, and high‐performance RAMs due to their large specific surface area, lightweight, excellent dielectric properties, high electrical conductivity, and stability under harsh conditions. This review begins with a brief history of stealth technology and an introduction to electromagnetic waves, radar systems, and radar absorbing materials. This is followed by a discussion of recent research progress in carbon‐based RAMs, including carbon blacks, carbon fibers, carbon nanotubes, graphite, graphene, and MXene, along with an in‐depth examination of the principles and strategies on electromagnetic attenuation characteristics. Hope this review will offer fresh perspectives on the design and fabrication of carbon‐based RAMs, thereby fostering a deeper fundamental understanding and promoting practical applications. [ABSTRACT FROM AUTHOR]

A novel photonic terahertz measurement system based on a frequency‐modulated continuous‐wave (FMCW) radar approach is presented. In previous works, fast frequency modulation has been demonstrated in connection with a continuous wave terahertz spectroscopy setup based on the photomixing principle. In this paper, a terahertz radar based on both a photomixing transmitter and a photomixing receiver, in contrast to the rigid spectroscopy approach, is reported. Hereby, frequency modulation bandwidths of more than 1.65 THz in radar operation is achieved. This corresponds to an order of magnitude more than what is previously achieved by terahertz radar systems. At the same time, measurement rates can be achieved that are comparable with radar systems based on Monolithic Microwave Integrated Circuits (MMICs) according to the current state of the art. Within the scope of the work, two operating modes are realized, one with a measurement rate of about 560 Hz at 600 GHz modulation bandwidth and one with 200 Hz at 1.65 THz modulation bandwidth, which can be set within the spectrum from 50 GHz to about 4.5 THz. The possibility to adjust the operating range of the radar without necessary hardware adaptations is another unique feature of the presented system, which allows the operator to choose a suitable frequency band that corresponds best to a certain measurement scheme via software settings. Besides the potential for multi‐layer thickness inspections, the capabilities of this technique for terahertz imaging applications are presented. [ABSTRACT FROM AUTHOR]

This paper outlines the construction of a new Multiplatform Precipitation Feature (MPF) framework designed to facilitate storm‐based analyses of data sets from multiple independent observing platforms for convective studies. The MPF approach is applied to ground‐ and space‐based precipitation observations and retrievals from the Global Precipitation Measurement mission Validation Network with space‐based lightning measurements from the Lightning Imaging Sensor on board the International Space Station to create a prototype MPF database. The data are synthesized in a thunderstorm‐like, feature‐based framework that enables broad statistical analyses of the storm‐based relationships between the microphysics, kinematics, and electrical properties of convection that can contribute insight into their morphological, regional, and seasonal variations. This manuscript details how MPFs are defined from precipitation data and how observations from multiple additional platforms are combined within the context of an MPF thunderstorm boundary and used to characterize the feature. A demonstration of the prototype database as a proof‐of‐concept showcases its flexibility to conduct both large‐scale storm‐based statistical studies as well as MPF‐facilitated thunderstorm case studies. Key Points: Demonstration database of individual thunderstorm‐like features combines independent data from multiple moving and fixed observing platformsPreliminary features include precipitation and lightning data collected from Global Precipitation Measurement and Lightning Imaging Sensor on board the International Space Station satellite instruments and ground‐based radarsDatabase enables cross‐platform, broad statistical thunderstorm‐centric analyses as well as detailed single‐storm case study reconstruction [ABSTRACT FROM AUTHOR]

To address the problems of traditional two‐level nested arrays (TTNA) with low degrees of freedom and high mutual coupling, a generalised two‐level nested MIMO radar array with high degrees of freedom and low mutual coupling for the direction of arrival (DOA) estimation is proposed. First, two coprime expansion factors are introduced in the TTNA to mitigate mutual coupling among array elements and enhance its degrees of freedom. Secondly, the range of consecutive lags and the total number of virtual array elements are derived in closed form under the structures of 'sum–difference co‐array'. Finally, utilising the atomic norm minimisation theory, the authors address the issue of discrete holes caused by different coprime expansion factors through filling in virtual arrays at discontinuities. Furthermore, a convex optimisation model to recover equivalent received signals from these filled virtual arrays is established, which is combined with a multiple signal classification algorithm for DOA estimation. Simulation results validate the rationality of the array structure and demonstrate the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Based on quantum illumination (QI) protocol, microwave quantum radar has been developed, which has attracted great interest from radar researchers. While, according to the QI protocol, the quantum advantage of QI radar is related to the power of generated signal, which may lead to a trade‐off between its quantum advantage and the maximum detection range. However, the maximum detection range is an important indicator to measure the performance of the radar. Therefore, it is necessary to quantitatively analyse the maximum detection range and quantum advantages of microwave QI radar, which will help us understand its performance from the perspective of radar applications and further evaluate the potential application scenarios and future research directions of it. The microwave quantum radar equation is developed, by the authors, based on the classical radar equation with the consideration of the quantum properties of extremely weak signals. Then, the maximum detection range of QI radar and classical two mode noise radar in different microwave bands is comprehensively compared and analysed. The possible applications of microwave QI radars in different bands are also discussed. In addition, the authors demonstrate that the symmetrical quantum limited amplification actually has no contribution to improving the detection range of a QI radar. [ABSTRACT FROM AUTHOR]

The usual procedure for extracting ocean surface information from data acquired from a high‐frequency surface wave radar transmitting from a floating platform is to first compensate for the motion of the antenna in the acquired motion‐contaminated Doppler spectrum and then extract the ocean wave parameters from the motion‐compensated result. The authors propose a new real‐time method to estimate the significant wave height directly from the antenna's received electric field in the time‐domain without requiring prior knowledge of the motion parameters or performing motion compensation. Based on the relation between the ocean surface displacement and the received electric field, this method calculates the significant wave height from the windowed variance of the upper envelope of the received electric field. A preliminary calibration is required, which can be carried out either by the deployment of a wave buoy or by analysing the data over a time period during which the sea state varies. The results from this simple proposed technique show that it may be used to estimate the significant wave height with a root‐mean‐square error (RMSE) between 10 and 14 cm over a wide range of significant wave height values. [ABSTRACT FROM AUTHOR]

The Gaussian noise model and estimators based on least squares (LS) are widely used in target localisation with distributed multiple‐input multiple‐output (MIMO) radar because of their computational efficiency. However, the accuracy of existing LS‐based target localisation algorithms deteriorates sharply in the presence of outliers in the measurements. Thus, a robust solution is developed based on the lp ${l}_{p}$‐norm minimisation criterion and iteratively reweighted least squares (IRLS) for locating a moving target with impulse noise using the angle of arrival (AOA), time delay (TD), and Doppler shift (DS) measurements. First, the AOA, TD, and DS measurement noise models are developed based on the α‐stable distribution. Then, the localisation problem is transformed into an lp ${l}_{p}$‐norm minimisation problem by linearising the AOA, TD, and DS measurement equations. Finally, the lp ${l}_{p}$‐norm minimisation problem is solved using an IRLS method to obtain the target position and estimate the velocity. Moreover, the optimum of the norm order (p) and the Cramér–Rao lower bound for the target position and velocity estimation are derived under α‐stable distributed measurement noise. The simulation results demonstrate that the developed algorithm offers higher accurascy and robustness than the existing ones in the presence of measurement outliers. [ABSTRACT FROM AUTHOR]

Traditional signal deinterleaving methods depend on preset parameters, use complicated steps and have limitations in handling complex electromagnetic environments where signals overlap in the space, time and frequency domains. This paper presents a novel approach using a deep segmentation network for radar signal deinterleaving. The pulse descriptor word data is transformed into a dot matrix image, which is then processed by a Cascade‐Recurrent Loop Network (CRLN) for segmentation. The CRLN consists of a Type Segmentation Network, an Amount Decision Network and a Recurrent Individual Segmentation Network. By recursively segmenting each target and determining the number of sources, the CRLN overcomes the challenges posed by the discontinuity and high overlap of individuals in the dot matrix image. Experimental results demonstrate the effectiveness of the proposed method, surpassing traditional deinterleaving methods, achieving high accuracy, low omission rate and effectively mitigates the increasing batch problem in complex scenarios. The experiments conducted on a randomly generated dataset yielded impressive results: over 90% accuracy when dealing with pulses from 15 radars of different types, over 96% accuracy when deinterleaving pulses from 4 radar individuals of a same type and over 90% accuracy when handling 5 radars of different types with 10% pulse loss. [ABSTRACT FROM AUTHOR]

Snow depth on sea ice is an Essential Climate Variable and a major source of uncertainty in satellite altimetry‐derived sea ice thickness. During winter of the MOSAiC Expedition, the "KuKa" dual‐frequency, fully polarized Ku‐ and Ka‐band radar was deployed in "stare" nadir‐looking mode to investigate the possibility of combining these two frequencies to retrieve snow depth. Three approaches were investigated: dual‐frequency, dual‐polarization and waveform shape, and compared to independent snow depth measurements. Novel dual‐polarization approaches yielded r2 values up to 0.77. Mean snow depths agreed within 1 cm, even for data sub‐banded to CryoSat‐2 SIRAL and SARAL AltiKa bandwidths. Snow depths from co‐polarized dual‐frequency approaches were at least a factor of four too small and had a r2 0.15 or lower. r2 for waveform shape techniques reached 0.72 but depths were underestimated. Snow depth retrievals using polarimetric information or waveform shape may therefore be possible from airborne/satellite radar altimeters. Plain Language Summary: Data collected using a surface‐based radar instrument on sea ice during the MOSAiC Arctic expedition were used to develop new techniques to estimate the depth of the overlying snow. We used different polarizations of the radiation to detect the depths of the upper and lower snow surfaces, and subtracted them to give snow depth. These depths agreed well with an independently collected snow depth data set. Estimates of snow depth using two different radar frequencies were less accurate, whilst using information of the shape of the returning pulse of radiation also showed a relationship with the independent snow depths, though not as strong as the polarization method. These results indicate that polarimetry (using a new satellite mission) and/or waveform shape (using existing missions) could be used to estimate snow depth on sea ice from airborne or satellite platforms. Key Points: Novel polarization‐based snow depth estimation techniques were developed using surface‐based Ku‐ and Ka‐band polarimetric radar altimeter dataThe dominant scattering surface was the air/snow and snow/ice interface in co‐ and cross‐polarized data, respectively, at both frequenciesRadar‐derived snow depths agreed with independent measurements, with r2 up to 0.77 and accuracy of 1 cm for best‐performing techniques [ABSTRACT FROM AUTHOR]

The coupling at the interface between tectonic plates is a key geophysical parameter to capture the frictional locking across plate boundaries and provides a means to estimate where tectonic strain is accumulating through time. Here, we use both interferometric radar (InSAR) and Global Navigation Satellite System (GNSS) data to investigate the plate coupling of the Hikurangi subduction zone beneath the North Island of New Zealand, where multiple slow slip cycles are superimposed on the long‐term loading. We estimate the plate coupling across the subduction zone over three multi‐year observational periods targeting different stages of the slow slip cycle. Our results highlight the importance of the observational time period when interpreting coupling maps, emphasizing the temporal variability of plate coupling. Leveraging multiple geodetic data sets, we demonstrate how InSAR provides powerful constraints on the spatial resolution of both plate coupling and slow fault slip, even in a region where a dense GNSS network exists. Plain Language Summary: Plate coupling as a concept describes to what degree the boundaries between tectonic plates are locked and building up stress. Such accumulated stress (over hundreds to thousands of years) will eventually be released in earthquakes, and therefore provides important information about the potential for future earthquakes. Our study uses satellite data to investigate how coupling between tectonic plates along the Hikurangi subduction zone (New Zealand's largest and most dangerous plate boundary fault) changes with time. We analyzed Interferometric Synthetic Aperture Radar and Global Navigation Satellite System data to map the areas where the plates are stuck together (coupled) and where they move past each other (uncoupled). We show that plate coupling varies significantly in space over 2, 4, and 10‐year time scales, highlighting the importance of carefully considering the observational time period when interpreting coupling maps. Key Points: Integration of high‐resolution displacement maps from radar imagery captures plate coupling at fine scalesEstimates of plate coupling depend strongly on the time period over which surface velocities are measuredTemporal variations in plate coupling highlight when and where slow slip dominates the slip budget [ABSTRACT FROM AUTHOR]

Riming is a key process of precipitation formation in ice-containing clouds, but quantifying riming from observations is challenging, limiting our ability to evaluate the riming process in numerical weather models. One challenge for radar observations is that riming changes both the physical properties (mass, area cross-section) and scattering properties of ice particles. These changes need to be implemented consistently as a function of riming in radar forward operators, which are required for retrievals and model evaluation in observation space. In this study, mass-size, cross-section area-size, and backscattering cross-section relations are developed as a function of the normalized rime mass for aggregates composed of various monomer types (columns, dendrites, needles, plates, and rosettes). The proposed framework allows us to simulate scattering properties of aggregated ice particles consistently as a function of riming in retrievals and radar forward operators. The parameterizations are developed from a large data set of simulated rimed aggregates of different sizes and monomer crystal types. The backscattering cross-section parameterization (the "riming-dependent parameterization") is evaluated for radar frequencies of 35.6 and 94.0 GHz and is based on the Self-Similar Rayleigh-Gans approximation (SSRGA), which is increasingly used to calculate microwave scattering of ice crystals and snowflakes. Compared with parameterizations from the literature that do not consider riming, the riming-dependent parameterization leads to significantly smaller biases in terms of backscattering cross-section. When using the particle masses and scattering properties of the individual particles simulated by the aggregation and riming model as a reference, the bias of our parameterization is below 1 dB when integrating over an exponential particle size distribution with sizes from 0.1-10 mm. [ABSTRACT FROM AUTHOR]

Although cloud-affected satellite observations are heavily used for nowcasting applications, their use in regional data assimilation is very limited despite possible benefits for convective-scale forecasts. In this article, we estimate the potential impact of assimilating cloud-affected satellite observations of visible (0.6 µm) and near thermal infrared wavelengths (6.2 and 7.3 µm) relative to the impact of assimilating radar reflectivity observations. We employed observing-system simulation experiments with a perfect-model forecast for two cases of strong convective summertime precipitation. Observations are simulated using the radiative transfer model RTTOV/MFASIS and assimilated by the ensemble adjustment Kalman filter in the Data Assimilation Research Testbed. The Weather Research and Forecasting model at 2-km grid resolution was used for forecasts. Results show that satellite observations can be nearly as beneficial as three-dimensional radar reflectivity observations. Under favorable conditions, where the prior contains no error in the stage of storm development but only in horizontal position and strength, the assimilation of visible observations leads to 88% of the radar impact. Under more difficult conditions, the impact of visible and infrared observations still reached 50 and 79%, respectively. [ABSTRACT FROM AUTHOR]