Radar Network Research Articles

A 3D U-Net Based on a Vision Transformer for Radar Semantic Segmentation.

Radar data can be presented in various forms, unlike visible data. In the field of radar target recognition, most current work involves point cloud data due to computing limitations, but this form of data lacks useful information. This paper proposes a semantic segmentation network to process high-dimensional data and enable automatic radar target recognition. Rather than relying on point cloud data, which is common in current radar automatic target recognition algorithms, the paper suggests using a radar heat map of high-dimensional data to increase the efficiency of radar data use. The radar heat map provides more complete information than point cloud data, leading to more accurate classification results. Additionally, this paper proposes a dimension collapse module based on a vision transformer for feature extraction between two modules with dimension differences during dimension changes in high-dimensional data. This module is easily extendable to other networks with high-dimensional data collapse requirements. The network's performance is verified using a real radar dataset, showing that the radar semantic segmentation network based on a vision transformer has better performance and fewer parameters compared to segmentation networks that use other dimensional collapse methods.

Optimal Use of Radar Radial Winds in the HARMONIE Numerical Weather Prediction System

Abstract The use of radial velocity information from the European weather radar network is a challenging task, because of a heterogeneous radar network and the different ways of providing the Doppler velocity information. Preprocessing is therefore needed to harmonize the data. Radar observations consist of a very high resolution dataset, which means that it is both demanding to process as well as that the inherent resolution is much higher than the model resolution. One way of reducing the number of data is to create “super observations” (SO) by averaging observations in a predefined area. This paper describes the preprocessing necessary to use radar radial velocities in the data assimilation where the SO construction is included. Our main focus is to optimize the use of radial velocities in the HARMONIE–AROME numerical weather model. Several experiments were run to find the best settings for first-guess check limits as well as a tuning of the observation error value. The optimal size of the SO and the corresponding thinning distance for radar radial velocities was also studied. It was found that the radial velocity information and the reflectivity from weather radars can be treated differently when it comes to the size of the SO and the thinning. A positive impact was found when adding the velocities together with the reflectivity using the same SO size and thinning distance, but the best results were found when the SO and thinning distance for the radial velocities are smaller than the corresponding values for reflectivity.

Networked Radar Mission Planning Algorithm Based on Pulse Interleaving and Cross-Scheduling Interval

Aiming at the problem of networked radar task planning, this paper proposes a task scheduling algorithm based on pulse interleaving and cross-scheduling interval from the perspective of rationally arranging communication and detection tasks, improving the utilization rate of radar system time resources and the success rate of task scheduling. The waiting period of tracking tasks is fully utilized to sort the tasks by pulse interleaving. On the other hand, taking advantage of the characteristics of large search task cycle and long timeliness, when there are still idle time slices in the current scheduling cycle that are not used, the time axis can be opened, and the subsequent scheduling interval can be The search tasks in the interval are advanced to be executed within the current scheduling cycle. The simulation results show that the algorithm can effectively improve the time resource utilization rate and task scheduling success rate of the system.

Multi‐Instrument Observations of the Evolution of Polar Cap Patches Associated With Flow Shears and Particle Precipitation

AbstractSimultaneous observations from Defense Meteorological Satellite Program, Swarm, Resolute Bay all‐sky imagers, GPS Total Electron Content and Super Dual Auroral Radar Network, are used to investigate the evolution and key characteristics of the Tongue of Ionization (TOI) being restructured into a polar cap patch. Six satellites crossed the TOI of patch as it moved from the dayside to the nightside. It was initially hot, then a mix of both cold and hot, and finally it became a cold patch. This suggests that cold patch is not only a result of solar extreme ultraviolet radiation, but may also develop when a hot patch cools down. Soft‐electron precipitation and flow shears both contribute to the TOI restructuring and the appearance of polar cap patch. The plasma density of patch at ∼500 km was at least 4 times higher than at ∼800 km. The plasma density enhancement gradually decreased as the patch evolved due to decreased production and transport of cold nightside low‐density plasma. Moreover, the duskward motion of the patch was influenced by changes in the ionospheric convection pattern.

Optimal Polarization Design for Distributed MIMO Radar Networks: A Stackelberg Game Perspective

Optimal Polarization Design for Distributed MIMO Radar Networks: A Stackelberg Game Perspective

Observations of ULF Pulsations During TRINNI Events

AbstractTwo magnetospheric phenomena are commonly observed in both SuperDARN (Dual Auroral Radar Network) and ground‐based magnetometer network data. These are ULF pulsations driven by magnetohydrodynamic waves, and flow bursts in the high latitude ionosphere driven by magnetic reconnection. Possible connections between these two phenomena are investigated. TRINNI (Tail Reconnection during IMF Northward, Non‐substorm Intervals) events, high‐speed ionospheric flows resulting from magnetotail reconnection during IMF Bz‐positive but By‐dominant periods, have been investigated by many researchers. These events produce measurable enhancements of the cross polar cap potential. Four previously reported TRINNI events have been analyzed using SuperDARN data, together with high‐latitude magnetometer data which show simultaneous ULF pulsations. Fourier analysis of the cross polar cap potential measured by SuperDARN and two magnetic field components revealed common spectral peaks in the 1–5 mHz range. The signals were narrow‐band filtered and complex demodulation was performed to construct the analytic signals, allowing determination of amplitude and phase. In all four events the magnetic field and potential signals showed similar wave packet structure with large‐amplitude oscillations at a common frequency for periods of the order of hours. During these periods the phase differences between the field and potential remained constant. It is argued that this phase locking suggests a causal link between the two oscillations, so that the TRINNIs could be the source of the magnetic pulsations, or they at least share a common driver.

Model- and Deep Learning-Based Bandwidth and Carrier Frequency Allocation in Distributed Radar Networks

Model- and Deep Learning-Based Bandwidth and Carrier Frequency Allocation in Distributed Radar Networks

3-D Target Tracking for Distributed Heterogeneous 2-D–3-D Passive Radar Network

3-D Target Tracking for Distributed Heterogeneous 2-D–3-D Passive Radar Network

Precipitation Vertical Structure Characterization: A Feature-Based Approach

Abstract The three-dimensional (3D) structure of precipitation systems is highly dependent on hydrometeor formation processes and microphysics. This study aims to characterize distinct vertical profiles of precipitation regimes by relying on the availability of a high-quality, spatially dense radar network and its capability to observe the 3D structure of the storms. A deep-learning-based framework, coupled with unsupervised clustering methods, is developed to identify types of precipitation structures irrespective of their physical properties. A 6-month period of 3D reflectivity profiles from the Multi-Radar Multi-Sensor (MRMS) network is used to identify different regimes and investigate their properties with respect to the underlying environmental conditions. Dominant features retrieved from radar reflectivity profiles using convolutional neural-network-based autoencoders are employed to identify similar-looking vertical structures using coupled k-means and agglomerative clustering algorithms. The k-means method identifies distinct groups, while the agglomerative clustering visualizes intercluster relationships. The framework identifies 18 clusters that can be broadly combined into five groups of varied echo-top heights. The 18 clusters demonstrate variability with respect to structural features and precipitation rate/type, implying that profiles in each group belong to a physically different precipitation regime. An independent analysis of the regime properties is conducted by matching the MRMS reflectivity profiles with environmental parameters derived from the High-Resolution Rapid Refresh model forecasts. The distribution of the environmental variables confirms cluster-specific feature properties, confirming the physics-based regime separation across the clusters and their dependence on the vertical structure. The identified precipitation regimes can assist in developing physics-guided retrievals and studying precipitation regimes. Significance Statement This study proposes a systematic model to identify precipitation profiles of distinct vertical structures and evaluate their dependence on environmental conditions. The model was developed using ground-based radar observations; however, there is potential to extend this model to reflectivity profiles from both ground- and satellite-based sensors. In addition, the identified precipitation regime clusters could be a proxy for the vertical structure of precipitation systems and assist in determining the structural variability within traditional precipitation type classification (e.g., convective versus stratiform). Moreover, identifying the precipitation regimes could also be used to improve satellite-based precipitation retrievals. Finally, a better understanding of precipitation structure would also help improve the initialization of climate models.

On the Creation, Depletion, and End of Life of Polar Cap Patches

AbstractIonospheric 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.

Contactless Heart and Respiration Rates Estimation and Classification of Driver Physiological States Using CW Radar and Temporal Neural Networks

The measurement and analysis of vital signs are a subject of significant research interest, particularly for monitoring the driver’s physiological state, which is of crucial importance for road safety. Various approaches have been proposed using contact techniques to measure vital signs. However, all of these methods are invasive and cumbersome for the driver. This paper proposes using a non-contact sensor based on continuous wave (CW) radar at 24 GHz to measure vital signs. We associate these measurements with distinct temporal neural networks to analyze the signals to detect and extract heart and respiration rates as well as classify the physiological state of the driver. This approach offers robust performance in estimating the exact values of heart and respiration rates and in classifying the driver’s physiological state. It is non-invasive and requires no physical contact with the driver, making it particularly practical and safe. The results presented in this paper, derived from the use of a 1D Convolutional Neural Network (1D-CNN), a Temporal Convolutional Network (TCN), a Recurrent Neural Network particularly the Bidirectional Long Short-Term Memory (Bi-LSTM), and a Convolutional Recurrent Neural Network (CRNN). Among these, the CRNN emerged as the most effective Deep Learning approach for vital signal analysis.

Anti-jamming power allocation scheme for a multi-static MIMO radar network based on mutual information

We investigate a power allocation scheme for a multi-static multiple-input multiple-output(MIMO) radar network with the influence of a jammer. We consider a radar network organized into multiple statistical MIMO radars without communication and coordination. The conflict between the jammer and the radar network is described as a two-player zero-sum game because of their adversarial nature. The mutual information(MI) is used as the utility function where the radar network can change the power allocation strategy so that the MI is optimized. In order to solve the power allocation problem, we combine the particle swarm optimization(PSO) algorithm and the water-filling strategy. The PSO algorithm is used to optimize the total power of each MIMO radar in the radar network, and the water-filling strategy is used to optimize the antenna power configuration in each MIMO radar. Finally, we present the simulation results, compare the performances to those of other strategies, and evaluate the affecting elements.

Evaluation of Rain Estimates from Several Ground-Based Radar Networks and Satellite Products for Two Cases Observed over France in 2022

The recent development of satellite products for observing precipitation based on different technologies (microwaves, infrared, etc.) allows for near-real-time meteorological studies. The purpose of this article is to evaluate 11 satellite products (GHE, PDIR, IMERG-Early v6, IMERG-Late v6, CMORPH v0.x, CMORPH-RT v0.x, GSMaP-NRT v7, GSMaP-NRT-GC v7, GSMaP-NOW v7, GSMaP-NOW-GC v7, and DATABOURG) currently available and compare them to 2 ground-based radar networks (PANTHERE and OPERA) and the French rain-gauge network RADOME. Two case studies of intense precipitation over France (22 to 25 April 2022 and 24 to 29 June 2022) were selected. The radar estimations are closer to the RADOME observations than the satellite-based estimations, which tend to globally underestimate the precipitation amounts over the areas of interest while OPERA tends to strongly overestimate precipitation amounts during the June case study. The PANTHERE radar product and the carrier-to-noise product DATABOURG shows promising results. Near-real-time satellite products tend to have closer precipitation amounts to the reference dataset than satellite products with a shorter latency. The use of these datasets for nowcasting developments is plausible but further analyses must be conducted beforehand.

Automatic cumulonimbus and towering cumulus identification based on the Italian weather radar network data

AbstractWe present an automatic cumulonimbus and towering cumulus cloud identification method, based on the Italian weather radar network data coupled with a NWP model and ground station data. In particular, we focus on its aeronautical applications as a support for the aerodrome automated weather observation systems and METAR AUTO reports. In addition to the significant convective cloud detection and discrimination, we also present a new methodology for convective cloud coverage and ceiling estimations.

Fully digital, urban networked staring radar: Simulation and experimentation

AbstractThe application and uses of drones in all areas are continuously rising, especially in civilian use cases. This increasing threat requires reliable drone surveillance in urban environments. Radar is the obvious candidate with its ability to detect small objects at range, in all weather conditions. The use of an L‐band networked radar for urban sensing of S‐UAS targets is explored. Small echoes from S‐UAS places a premium on synchronisation, which is the fundamental key for high performance networked radar. The effect of timing errors on the operation of the network radar is investigated theoretically and experimentally, and the processing tools for synchronising data based on the direct signal returns of the transmitter are developed. Also, drone detection using bistatic L‐band staring radar is achieved both in simulation and then in real field trials where the SNR and detection performance are computed and analysed. The updated direct signal synchronisation method for bistatic staring radar is shown to provide comparable SNR and positional accuracy for S‐UAS targets as the monostatic staring radar.

A modern approach with follower-leading clustering algorithm for visualizing author collaborations and article themes in skin cancer research: A bibliometric analysis.

Skin cancers (SCs) arise due to the proliferation of atypical cells that have the potential to infiltrate or metastasize to different areas of the body. There is a lack of understanding regarding the country-based collaborations among authors (CBCA) and article themes on SCs. A clustering algorithm capable of categorizing CBCA and article themes on skin cancer is required. This study aimed to apply a follower-leading clustering algorithm to classify CBCA and article themes and present articles that deserve reading in recent ten years. Between 2013 and 2022, a total of 6526 articles focusing on SC were extracted from the Web of Science core collection. The descriptive, diagnostic, predictive, and prescriptive analytics model was employed to visualize the study results. Various visualizations, including 4-quadrant radar plots, line charts, scatter plots, network charts, chord diagrams, and impact beam plots, were utilized. The category, journal, authorship, and L-index score were employed to assess individual research achievements. Diagnostic analytics were used to cluster the CBCA and identify common article themes. Keyword weights were utilized to predict article citations, and noteworthy articles were highlighted in prescriptive analytics based on the 100 most highly cited articles on SC (T100SC). The primary entities contributing to SC research include the United States, the University of California, San Francisco in US, dermatology department, and the author Andreas Stang from Germany, who possess higher category, journal, authorship, and L-index scores. The Journal of the American Academy of Dermatology has published the highest number of articles (n = 336, accounting for 5.16% of the total). From the T100SC, 7 distinct themes were identified, with melanoma being the predominant theme (92% representation). A strong correlation was observed between the number of article citations and the keyword weights (F = 81.63; P < .0001). Two articles with the highest citation counts were recommended for reading. By applying the descriptive, diagnostic, predictive, and prescriptive analytics model, 2 noteworthy articles were identified and highlighted on an impact beam plot. These articles are considered deserving of attention and could potentially inspire further research in the field of bibliometrics, focusing on relevant topics related to melanoma.

Forecast Applications of GLM Gridded Products: A Data Fusion Perspective

Abstract The recently deployed GOES-R series Geostationary Lightning Mapper (GLM) provides forecasters with a new, rapidly updating lightning data source to diagnose, forecast, and monitor atmospheric convection. Gridded GLM products have been developed to improve operational forecast applications, with variables including flash extent density (FED), minimum flash area (MFA), and total optical energy (TOE). While these gridded products have been evaluated, there is a continual need to integrate these products with other datasets available to forecasters such as radar, satellite imagery, and ground-based lightning networks. Data from the Advanced Baseline Imager (ABI), Multi-Radar Multi-Sensor (MRMS) system, and one ground-based lightning network were compared against gridded GLM imagery from GOES-East and GOES-West in case studies of two supercell thunderstorms, along with a bulk study from 13 April to 31 May 2019, to provide further validation and applications of gridded GLM products from a data fusion perspective. Increasing FED and decreasing MFA corresponded with increasing thunderstorm intensity from the perspective of ABI infrared imagery and MRMS vertically integrated reflectivity products, and was apparent for more robust and severe convection. Flash areas were also observed to maximize between clean-IR brightness temperatures of 210–230 K and isothermal reflectivity at −10°C of 20–30 dBZ. TOE observations from both GLMs provided additional context of local GLM flash rates in each case study, due to their differing perspectives of convective updrafts. Significance Statement The Geostationary Lightning Mapper (GLM) is a lightning sensor on the current generation of U.S. weather satellites. This research shows how data from the space-based lightning sensor can be combined with radar, satellite imagery, and ground-based lightning networks to improve how forecasters monitor thunderstorms and issue warnings for severe weather. The rate of GLM flashes detected and the area they cover correspond well with radar and satellite signatures, especially in cases of intense and severe thunderstorms. When the GLM observes the same thunderstorm from the GOES-East and GOES-West satellites, the optical energy (brightness) of the flashes may help forecasters interpret the types of flashes observed from each sensor.

Analysis of UAV Radar and Communication Network Coexistence With Different Multiple Access Protocols

Analysis of UAV Radar and Communication Network Coexistence With Different Multiple Access Protocols

Increasing of the accuracy of determining the coordinates of an aerial object in the two-position network of small-sized radars

The object of research is the process of determining the coordinates of aerial objects by a small-sized radars. The main hypothesis of the study assumes that combining two small-sized radars into a network could improve the accuracy of determining the coordinates of aerial objects. It was established that when determining the coordinates of an aerial object by a small-sized radars, the accuracy of determining the range is much better than the accuracy of determining the angular coordinate. To eliminate this shortcoming, a two-position network of small-sized radars and their error ellipses was considered. It is proposed to use the range-finding method in each small-sized radar station of the two-position network. A method for determining the coordinates of aerial objects in a two-position network of small-sized radars has been improved, which, unlike known ones: – enables a synchronous survey of the airspace by small-sized radars; – measures the range to the aerial object by two small-sized radars; – determines the coordinates of the aerial object by the joint processing of radar information from two small-sized radars. The accuracy of determining the coordinates of aerial objects in a two-position network of small-sized radars was evaluated. The experimental evaluation was carried out by means of simulation using the method of Monte Carlo statistical tests. Working areas of the two-position network of small-sized radars were calculated. It was established that the two-position network of small-radars works only in the area formed by the intersections of the viewing areas of small-sized radars during their autonomous operation.

Deployable optics for the Buccaneer Main Mission (BMM)

AbstractDefence Science and Technology Group (DSTG) is currently preparing for the launch of the Buccaneer Main Mission (BMM) satellite, the successor to the Buccaneer Risk Mitigation Mission (BRMM). BMM hosts a high-frequency (HF) antenna and receiver to contribute to the calibration of the Jindalee Operational Radar Network (JORN). Verification of the successful deployment and stability of the large HF antenna is critical to the success of the mission. A bespoke deployable optics payload has been developed by DSTG to fulfil the dual purpose of direct verification of the deployed state of the HF antenna and capturing images of the Earth through a rotatable, dual-surfaced mirror and a variable-focus liquid lens. The payload advances research at DSTG in several fields of space engineering, including deployable mechanisms, precision actuation devices, radiation-tolerant electronics, advanced metal polishing and optical metrology. This paper discusses the payload design, material selection, trade-offs considered for the deployable optics payload and preliminary test results.