Radar Information Research Articles

Justification of the criterion for technical synthesis of radar complexes for covert airspace control

For covert control of airspace, it is required to use complexes that meet the requirements for the effectiveness of obtaining data on objects in the field of observations and protection against the detection of their own unmasking signs by means of radio technical monitoring with predetermined indicators of resource intensity and efficiency of performing target functions. Joint fulfillment of these requirements when the parameters of the equipment structures and information processes are found under the established conditions for monitoring the properties of resource intensity and efficiency is achieved. Due to the complexity and inconsistency of the dependent bridges of the performance indicators and the stealth of application from the technical characteristics of the radar systems, it is necessary to substantiate the synthesis criterion for finding compromise technical solutions for the design of the equipment and the choice of a strategy for obtaining data on air facilities. The goal of the article is determination of design characteristics, frequency-time and energy signals parameters of radar complexes for analysis of air situation at concealment of unmasking features from means of radio technical monitoring. The criterion of technical synthesis of radar complexes for covert control of air-space is justified, which allows obtaining information about air objects at pre-established risks of threats to identify their own unmasking signs by means of radio technical monitoring. Regularities have been investigated and the conditions for achieving the target functions of complexes with resonant, band and frequency-independent signal transmission-reception devices have been determined. The principles of construction have been substantiated and the rules for finding rational parameters of the design of radar complexes and information processes used in airspace control in order to achieve the required operational properties of obtaining information about air objects while maintaining secrecy from radio monitoring facilities have been determined.

Joint Inversion of Sea Surface Wind and Current Velocity Based on Sentinel-1 Synthetic Aperture Radar Observations

Spaceborne synthetic aperture radar (SAR) has been proven to be a useful technique for observing the sea surface wind and current over the open ocean given its all-weather data-gathering capability and high spatial resolution. In addition to the commonly used radar return magnitude quantified by normalized radar cross section (NRCS), the Doppler centroid anomaly (DCA) has added another dimension of information. In this study, we combine the NRCS and DCA for a joint inversion of wind and surface current information using a Bayesian method. SAR-estimated Doppler is corrected by a series of steps, including the removal of scalloping effect and land correction. The cost function of this inversion scheme is constructed based on NRCS, DCA, and a background model wind. The retrieved wind results show the quality of performance through comparison with the in situ buoy measurements, showing a mean bias and a root-mean-square error (RMSE) of 0.33 m/s and 1.45 m/s for wind speed and 6.94° and 35.74° for wind direction, respectively. The correlation coefficients for wind speed and direction reach 0.931 and 0.661, respectively. Based on the obtained wind field, the line-of-sight velocity of the sea surface current is then derived by removing the wind contribution using the empirical model. The results show a consistent spatial pattern relative to the high-frequency radars, with the comparison relative to the drifter-measured current velocity exhibiting a mean bias of 0.02 m/s and RMSE of 0.32 m/s, demonstrating the reliability of the proposed inversion scheme. Such results will serve as a prototype for future spaceborne sensors to combine the radar return and Doppler information for the joint retrieval of wind vector and surface current velocity. This technique could be readily extended to the radar configuration of rotating beams for monitoring winds and current vectors.

Using a Combination of High-Frequency Coastal Radar Dataset and Satellite Imagery to Study the Patterns Involved in the Coastal Countercurrent Events in the Gulf of Cadiz

This study exploits the combination of High-Frequency Coastal Radar (HFR) information with satellite-derived observations to characterize the patterns involved in the coastal countercurrents (CCCs) events in the Gulf of Cadiz (GoC), which is situated in the SW of the Iberian Peninsula. The westward alongshore currents are observed throughout the year, but the main drivers necessary to develop this flow and its extension in both parts of the basin are not fully clear. In order to identify the main physical processes (both local and remote) that induce the development of these countercurrents and to evaluate the connection of the circulation patterns between the eastern and the western part of the GoC, we make use of several data sources available for the region. First of all, a land-based system of HFR antennas located at four different sites of the GoC provides the velocity field of the surface circulation of the basin. To achieve a significant characterization of the CCCs in the Gulf, the dataset analyzed is processed by means of a series of operations, including the Empirical Orthogonal Functions (EOFs) analysis used to identify spatial and temporal variability of the flow, a low-pass filter used to isolate the sub-inertial signal of the current and temporal interpolation to fill in the missing values. Secondly, given the known importance of the zonal component of the local winds combined with the variations in the mean pressure at sea level over the Western Mediterranean during these events, time series of meteorological data are processed and correlated with the current velocity series via a statistical analysis. Finally, sea surface temperature fields and chlorophyll-a distribution patterns are used as tracers to obtain information on the extension of the countercurrents where HFR data are missing in four cases studied during the year 2017. The conducted analysis revealed the consistent occurrence of westward coastal currents throughout the year, driven in the most intense cases by a combination of the zonal component of the local wind and atmospheric pressure fluctuations over the Western Mediterranean Sea. During those events, CCCs reached the Portuguese side of the Gulf and facilitated the advection of biological material and warmer waters.

3D Lightweight Interconnected Melamine Foam Modified with Hollow CoFe2O4/MXene toward Efficient Microwave Absorption.

Considering the increasing severity of electromagnetic wave pollution, the development of high-performance low-filler-content microwave absorbers possessing wide frequency bands and strong absorption for practical applications is a demanding research hotspot. In this study, from the perspectives of the electromagnetic component coordination and structural design, a three-dimensional (3D) interconnected CoFe2O4/MXene-melamine foam (MF) was constructed via simple impregnation and a single freeze-drying step. By changing the absorber (CoFe2O4/MXene) concentration, the pore opening and electromagnetic properties of the 3D foams can be effectively adjusted. When the absorber concentration is sufficiently high to clog the internal pores, the microwave absorption is hindered. When the filler (CoFe2O4/MXene-MF) content is just ∼5.8 wt % (at a density of ∼33.3 mg cm-3), a minimum reflection loss (RLmin) of -72.1 dB is achieved at a matching thickness of 3.32 mm, and an effective absorption bandwidth (4.54 GHz) covering the whole X band is achieved at a thickness of 3 mm. CoFe2O4/MXene-MF, which possesses a 3D porous electromagnetic network structure, optimizes impedance matching and enhances multiple polarization relaxations and reflections/scattering, resulting in superior absorption capabilities. In particular, the continuous network structure ensures the uniform distribution of electromagnetic fields in the microstructure, achieving high absorption at low filler contents. This work provides a reference for subsequent 3D absorber concentration studies and a novel engineering strategy for preparing a low-filler-content, lightweight, and efficient electromagnetic wave absorber, which could be applied in the fields of radar security and information communications.

Position and Orientation System Error Analysis and Motion Compensation Method Based on Acceleration Information for Circular Synthetic Aperture Radar

Circular synthetic aperture radar (CSAR) possesses the capability of multi-angle observation, breaking through the geometric observation constraints of traditional strip SAR and holding the potential for three-dimensional imaging. Its sub-wavelength level of planar resolution, resulting from a long synthetic aperture, makes CSAR highly valuable in the field of high-precision mapping. However, the motion geometry of CSAR is more intricate compared to traditional strip SAR, demanding high precision from navigation systems. The accumulation of errors over the long synthetic aperture time cannot be overlooked. CSAR exhibits significant coupling between the range and azimuth directions, making traditional motion compensation methods based on linear SAR unsuitable for direct application in CSAR. The dynamic nature of flight, with its continuous changes in attitude, introduces a significant deformation error between the non-rigidly connected Inertial Measurement Unit (IMU) and the Global Positioning System (GPS). This deformation error makes it difficult to accurately obtain radar position information, resulting in imaging defocus. The research in this article uncovers a correlation between the deformation error and radial acceleration. Leveraging this insight, we propose utilizing radial acceleration to estimate residual motion errors. This paper delves into the analysis of Position and Orientation System (POS) errors, presenting a novel high-resolution CSAR motion compensation method based on airborne platform acceleration information. Once the system deformation parameters are calibrated using point targets, the deformation error can be directly calculated and compensated based on the acceleration information, ultimately resulting in the generation of a high-resolution image. In this paper, the effectiveness of the method is verified with airborne flight test data. This method can compensate for the deformation error and effectively improve the peak sidelobe ratio and integral sidelobe ratio of the target, thus improving image quality. The introduction of acceleration information provides new means and methods for high-resolution CSAR imaging.

Simulating sub-hourly rainfall data for current and future periods using two statistical disaggregation models: case studies from Germany and South Korea

Abstract. The simulation of fast-reacting hydrological systems often requires sub-hourly precipitation data to develop appropriate climate adaptation strategies and tools, i.e. upgrading drainage systems and reducing flood risks. However, these sub-hourly data are typically not provided by measurements and atmospheric models, and many statistical disaggregation tools are applicable only up to an hourly resolution. Here, two different models for the disaggregation of precipitation data from a daily to sub-hourly scale are presented. The first one is a conditional disaggregation model based on first-order Markov chains and copulas (WayDown) that keeps the input daily precipitation sums consistent within disaggregated time series. The second one is an unconditional rain generation model based on a double Poisson process (LetItRain) that does not reproduce the input daily values but rather generates time series with consistent rainfall statistics. Both approaches aim to reproduce observed precipitation statistics over different timescales. The developed models were validated using 10 min radar data representing 10 climate stations in Germany and South Korea; thus, they cover various climate zones and precipitation systems. Various statistics were compared, including the mean, variance, autocorrelation, transition probabilities, and proportion of wet period. Additionally, extremes were examined, including the frequencies of different thresholds, extreme quantiles, and annual maxima. To account for the model uncertainties, 1000-year-equivalent ensembles were generated by both models for each study site. While both models successfully reproduced the observed statistics, WayDown was better (than LetItRain) at reproducing the ensemble median, showing strength with respect to precisely refining the coarse input data. In contrast, LetItRain produced rainfall with a greater ensemble variability, thereby capturing a variety of scenarios that may happen in reality. Both methods reproduced extremes in a similar manner: overestimation until a certain threshold of rainfall and underestimation thereafter. Finally, the models were applied to climate projection data. The change factors for various statistics and extremes were computed and compared between historical (radar) information and the climate projections at a daily and 10 min scale. Both methods showed similar results for the respective stations and Representative Concentration Pathway (RCP) scenarios. Several consistent trends, jointly confirmed by disaggregated and daily data, were found for the mean, variance, autocorrelation, and proportion of wet periods. Further, they presented similar behaviour with respect to annual maxima for the majority of the stations for both RCP scenarios in comparison to the daily scale (i.e. a similar systematic underestimation).

A generalized indicator of the efficiency of the use of the spacecraft radar to solve the remote sensing of earth problems by synthesizing the aperture. Part 1. Methodological foundations

The importance of the article is determined by the following factors: the increasing use of spacecraft radars and, at the same time, the extension of the range of tasks to be solved for remote sensing of the Earth; using a wide range of orbits for the output of remote sensing radars of the spacecraft; a large number of parameters of the remote sensing radar of the spacecraft, which determines the purposive characteristics in accordance with the tasks to be solved; conflicting requirements for parameter values when solving various remote sensing tasks. Under these conditions, the substantiation of the parameters of the onboard radar of the spacecraft, as well as any complex technical system, is impossible without a generalized indicator that takes into account heterogeneous and often contradictory requirements for their values. The methodological basis for the formation of a generalized indicator of the effectiveness of the use of space radar is to take into account the main objective characteristics of the remote sensing spacecraft, including: the area of the observed surface of the Earth per unit of time (productivity); spatial resolution along and across the path line; radiometric sensitivity, radiometric resolution. Using the specified objective characteristics and variable indicators of the degree to which each of them is built, a generalized indicator of the efficiency of the spacecraft is formed in the form of multipliers. The number of multipliers is equal to the number of objective characteristics. The first and second factors represent a weighted ratio (taking into account the corresponding exponents) of the first and second objective characteristics and determine the geometric component of the received volume of radar information. The third and fourth factors determine the possibility of radar discrimination of objects on the ground in terms of their reflectivity and represent a weighted ratio of radiometric sensitivity and radiometric resolution. On the basis of the radar equation, taking into account the relationship of the parameters of the spacecraft radar with the objective characteristics, analytical relations are obtained linking the parameters of the space radar and a generalized indicator of the effectiveness of the use of the space-based radar for remote sensing of Earth. This makes it possible to determine the requirements for the main resources of the on-board radar complex, namely, the radiated power and dimensions of the antenna system of the on-board radar.

Аномальный грозовой процесс на территории Ставропольского края

The paper analyzes the dynamics of such anomalous weather phenomenon as a thunderstorm. The thunderstorm process that took place in the North Caucasus on June 27, 2023 is considered. The thunderstorm process under consideration differed significantly from the statistical average, both in duration and in lightning parameters. The analysis was carried out using the example of the Stavropol Krai, although anomalous thunderstorms on June 27, 2023 also occurred in other regions of the North Caucasus. The work used the lightning direction network of the Federal State Budgetary Institution VGI with spatially separated sensors LS8000 and LS7002. To determine the characteristics of thunderstorm activity, the work used long-term data from the specified lightning direction network. Studies of thunderstorm activity were carried out in conjunction with the characteristics of convective clouds determined by MRL-5 weather radars. The Federal State Budgetary Institution VGI has organized the uptake, processing, archiving and transmission to the ASPD network of Roshydromet of radar information from all MRL-5 weather radars of the anti-hail services of the North Caucasus. During the day, about 30,000 lightning strikes were recorded by VGI in this territory. Of these, 3,713 were ground discharges. Of the registered 3,713 ground-based lightning discharges in the Stavropol Krai, negative lightning amounted to 2,901, and the positive one – 812. The share of negative lightning discharges from the total number of ground-based lightning discharges in the specified territory was 78%. The largest number of negative lightning (more than 70%) had values up to 20 kA. More than 40% of positive lightning discharges had values from 21 to 40 kA. The average value of currents of negative polarity was -18.6 kA, and that of positive polarity was +53.2 kA.

DEEP AND MACHINE LEARNING FOR MONITORING GROUNDWATER STORAGE BASINS AND HYDROLOGICAL CHANGES USING THE GRAVITY RECOVERY AND CLIMATE EXPERIMENT (GRACE) SATELLITE MISSION AND SENTINEL-1 DATA FOR THE GANGA RIVER BASIN IN THE INDIAN REGION

Abstract. Accurate estimation of groundwater levels in river basins is paramount for hydro-geological research and sustainable water resource management. In this paper, we introduce a deep learning framework explicitly developed for precise groundwater level estimation in the Ganga River Basin. Leveraging the combined band information of Sentinel-1 synthetic aperture radar (SAR) and GRACE satellite data, our approach capitalizes on the trans-formative capabilities of Vision Transformers (ViT) and their variants, with a particular focus on Swin-Transformer variant enriched with Normalization Attention Modules (NAMs).To address the unique challenges of the Ganga River Basin, we curated a comprehensive dataset, forming a robust foundation for training computer vision models tailored to this distinct geographical region. Through rigorous experiments, our state-of-the-art Vision Transformers demonstrated significant potential in groundwater level estimation, with the Swin-Transformer NAM-based model achieving an outstanding Mean Absolute Error (MAE) of 1.2. These remarkable results surpass conventional methodologies and underscore the substantial advancements achieved through advanced transformer-based architectures in this domain. Moreover, this research contributes a robust dataset for future endeavours, fostering further advancements in groundwater estimation and related fields. This study represents a substantial step towards advancing sustainable groundwater utilization practices in the Ganga River Basin and beyond.

A comprehensive multimodal dataset for contactless lip reading and acoustic analysis

Small-scale motion detection using non-invasive remote sensing techniques has recently garnered significant interest in the field of speech recognition. Our dataset paper aims to facilitate the enhancement and restoration of speech information from diverse data sources for speakers. In this paper, we introduce a novel multimodal dataset based on Radio Frequency, visual, text, audio, laser and lip landmark information, also called RVTALL. Specifically, the dataset consists of 7.5 GHz Channel Impulse Response (CIR) data from ultra-wideband (UWB) radars, 77 GHz frequency modulated continuous wave (FMCW) data from millimeter wave (mmWave) radar, visual and audio information, lip landmarks and laser data, offering a unique multimodal approach to speech recognition research. Meanwhile, a depth camera is adopted to record the landmarks of the subject’s lip and voice. Approximately 400 minutes of annotated speech profiles are provided, which are collected from 20 participants speaking 5 vowels, 15 words, and 16 sentences. The dataset has been validated and has potential for the investigation of lip reading and multimodal speech recognition.

Assimilation of 3D polarimetric microphysical retrievals in a convective-scale NWP system

Abstract. This study assimilates for the first time polarimetric C-band radar observations from the German meteorological service (DWD) into DWD's convective-scale model ICON-D2 using DWD's ensemble-based KENDA assimilation framework. We compare the assimilation of conventional observations (CNV) with the additional assimilation of radar reflectivity Z (CNV + Z), with the additional assimilation of liquid or ice water content (LWC or IWC) estimates below or above the melting layer instead of Z where available (CNV + LWC/Z or CNV + IWC/Z respectively). Hourly quantitative precipitation forecasts (QPF) are evaluated for two stratiform and one convective rainfall events in the summers of 2017 and 2021. With optimized data assimilation settings (e.g., observation errors), the assimilation of LWC mostly improves first-guess QPF compared with the assimilation of Z alone (CNV + Z), whereas the assimilation of IWC does not, especially for convective cases, probably because of the lower quality of the IWC retrieval in these situations. Improvements are, however, notable for stratiform rainfall in 2021, for which the IWC estimator profits from better specific differential phase estimates owing to a higher radial radar resolution than the other cases. The assimilation of all radar data sets together (CNV + LWC + IWC + Z) yields the best first guesses. All assimilation configurations with radar information consistently improve deterministic 9 h QPF compared with the assimilation of only conventional data (CNV). Forecasts based on the assimilation of LWC and IWC retrievals on average slightly improve Fraction Skill Score (FSS) and Frequency Bias (FBI) compared with the assimilation of Z alone (CNV + Z), especially when LWC is assimilated for the 2017 convective case and when IWC is assimilated for the high-resolution 2021 stratiform case. However, IWC assimilation again degrades forecast FSS for the convective cases. Forecasts initiated using all radar data sets together (CNV + LWC + IWC + Z) yield the best FSS. The development of IWC retrievals that are more adequate for convection constitutes one next step to further improving the exploitation of ice microphysical retrievals for radar data assimilation.

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.

Unmanned-Aerial-Vehicle-Aided Integrated Sensing and Computation With Mobile-Edge Computing

Integrated sensing and communication (ISAC), which enables the joint radar sensing and data communications, shows its great potential in many intelligent applications. In this paper, we investigate the unmanned aerial vehicle (UAV) aided ISAC with mobile edge computing (MEC), where the ISAC device deployed on the UAV senses multiple targets with the sensing scheduling and offloads the radar sensing data to the edge-server to train a machine learning model for target recognition. The radar estimation information rate is utilized to measure the radar sensing performance. We aim to minimize a system-wise cost that includes both the UAV’s energy consumption and the data collecting time, while satisfying the requirements on both the model training error and the radar sensing performance. We formulate a joint optimization problem of the sensing scheduling, the number of time-slots, the sensing power, the communication power, and the UAV trajectory. Despite the strict non-convexity of the formulated problem, we propose an efficient algorithm for solving it. Our algorithm jointly leverages the vertical decomposition that exploits the layered structure of the formulated problem and the horizontal decomposition that utilizes the block coordinate descent (BCD) method. Numerical results are presented to validate the effectiveness of our proposed algorithms and show the performance gain of our proposed scheme.

Non-contact human respiratory rate measurement based on two-level fusions of video and FMCW radar information

In this paper, we propose a robust respiratory rate (RR) measurement method using a two-level fusion of video and FMCW (frequency modulated continuous wave) radar information. Specifically, the video pixel displacement signals and the radar phase variation signals are extracted independently in shoulders and chest regions, which are then denoised by the serial-CEEMDAN (SCEEMDAN) method to obtain the corresponding single modal RR values. Next, at the feature level, the multivariate singular spectrum analysis (MSSA) is employed to extract the shared respiratory components in the video and radar modalities. Finally, at the decision level, we calculate the target RR value based on a signal-to-noise ratio (SNR) weighting of all single-modality and MSSA results. The proposed two-level fusion method is evaluated on a self-collected dataset, which includes 15 healthy subjects acting five challenging activities. The experimental results show that the proposed fusion method significantly outperform that of the single modality.

Robust Multiplexing Waveform Design for Integrated OFDM Radar and Communication via Complex Weight Optimization

Compared to traditional single orthogonal frequency division multiplexing (OFDM) radar and OFDM communication systems, integrated OFDM radar and communication systems have the advantages of improved sharing of scarce spectrum resources, a simple hardware structure and a reduced interference between signals. In this paper, a constraint relaxation-based robust OFDM multiplexing waveform (ROW-CR) design method for integrated radar and communication systems is proposed. Considering the influence of correlated clutter and jamming signals, transmission and reception models of different system platforms are established by allocating subcarrier complex weights of different antenna signals so as to determine radar conditional mutual information and communication channel capacity parameters. Meanwhile, a restricted closed model is introduced through the limit range of the target frequency response. Then, a robust OFDM waveform optimization problem for integrated radar and communication systems is constructed by the minimax criterion, and the closed-form solution is obtained by adopting an improved method based on trace function properties and constraint relaxation, resulting in a better radar and communication performance trade-off. In addition, a parameter hierarchical optimization-based robust OFDM waveform (ROW-PHO) design method is further explored to reduce the computational complexity, and this method can also ensure a low system performance loss. Finally, the numerical simulation results verify the effectiveness of the proposed methods.

Developing a Hail and Wind Damage Swath Event Database from Daily MODIS True Color Imagery and Storm Reports for Impact Analysis and Applications

Abstract Hail and damaging winds are two threats associated with intense and severe thunderstorms that traverse the Midwest and Great Plains during the primary growing season. In certain severe thunderstorm events, large swaths of agricultural crops are impacted, allowing the damage to be viewed from multiple satellite remote sensing platforms. Previous studies have focused on analyzing individual hail and wind damage swaths (HWDSs) using satellite remote sensing, but these swaths have never been officially archived or documented. This lack of documentation has made it difficult to analyze the spatial extent and temporal frequency of HWDSs from year to year. This study utilizes daily true color imagery from MODIS aboard NASA’s Terra and Aqua satellites and daily local storm reports from the Storm Prediction Center to build a database of HWDSs occurring in the months of May–August, for years 2000–20. This database identified 1646 HWDSs in 12 states throughout the Midwest and Great Plains, confirmed through a combination of archived severe weather warnings, radar information, and official storm reports. For each entry in the HWDS database, a geospatial outline is provided along with the most likely date of first visible damage from MODIS imagery as well as the physical characteristics and time of occurrence estimated from available warnings. This study also provides a summary of the radar characteristics for a portion of the database. This database will further the understanding of severe weather damage by hail and wind to agriculture to help understand the frequency of these events and assist in mapping the impacted areas. Significance Statement Hail and wind damage swaths (HWDSs) frequently occur during the primary growing season throughout the Midwest and Great Plains but are not yet officially documented or tracked like other severe weather impacts (e.g., tornadoes and derechos). This study describes the creation of a 21-yr HWDS event database using archived daily storm reports and daily true color satellite imagery. Once the database was completed and underwent quality checks, the research team identified spatial and temporal trends from the confirmed swaths.

Influence of Localized Rainfall Patterns on Landslide Occurrence—A Case Study of Southern Hiroshima with eXtended Radar Information Network Data during the July 2018 Heavy Rain Disasters

In this study, we use GIS and other analytical platforms to analyze the landslide distribution pattern in the July 2018 heavy rain disasters in the southern part of Hiroshima Prefecture in Japan in conjunction with chronological XRAIN (eXtended Radar Information Network) radar-acquired localized rainfall data in order to better understand the relationship between rainfall characteristics and landslide probability. An analysis of event rainfall from the July 2018 disasters determines that landslide-inducing rainfall started from 8:30 AM on 5 July and continued until 7:30 AM on 7 July, accumulating to up to 368 mm in total precipitation, and that there were two intensity peaks, one around 7:30 PM on 6 July, and another one around 4:30 AM on 7 July. These two events are associated with particularly high landslide activity, which indicates that landslide activation is related to peak-intensity rainfall combined with accumulated continuous precipitation. The XRAIN data were also used together with landslide reports to calculate the intensity–duration (i.e., I-D) rainfall threshold for the area. The mean annual precipitation in the whole study area ranged between 2025 mm and 3030 mm, with an average value of about 2300 mm. The spatial distribution of rainfall throughout the sampled years indicates that rainfall is remarkably localized, with higher values concentrated on elevated areas. However, it was also observed that the maximum precipitation volumes are not so closely related to landslide occurrence, and the highest landslide activity was found in intermediate precipitation class zones instead. Correlating the localization patterns of event precipitation and mean annual precipitation using Pearson’s correlation coefficient, we found an r value of 0.55, which is considered a moderate correlation between the two datasets (i.e., event precipitation and mean annual precipitation).

Multitarget-Tracking Method Based on the Fusion of Millimeter-Wave Radar and LiDAR Sensor Information for Autonomous Vehicles.

Multitarget tracking based on multisensor fusion perception is one of the key technologies to realize the intelligent driving of automobiles and has become a research hotspot in the field of intelligent driving. However, most current autonomous-vehicle target-tracking methods based on the fusion of millimeter-wave radar and lidar information struggle to guarantee accuracy and reliability in the measured data, and cannot effectively solve the multitarget-tracking problem in complex scenes. In view of this, based on the distributed multisensor multitarget tracking (DMMT) system, this paper proposes a multitarget-tracking method for autonomous vehicles that comprehensively considers key technologies such as target tracking, sensor registration, track association, and data fusion based on millimeter-wave radar and lidar. First, a single-sensor multitarget-tracking method suitable for millimeter-wave radar and lidar is proposed to form the respective target tracks; second, the Kalman filter temporal registration method and the residual bias estimation spatial registration method are used to realize the temporal and spatial registration of millimeter-wave radar and lidar data; third, use the sequential m-best method based on the new target density to find the track the correlation of different sensors; and finally, the IF heterogeneous sensor fusion algorithm is used to optimally combine the track information provided by millimeter-wave radar and lidar, and finally form a stable and high-precision global track. In order to verify the proposed method, a multitarget-tracking simulation verification in a high-speed scene is carried out. The results show that the multitarget-tracking method proposed in this paper can realize the track tracking of multiple target vehicles in high-speed driving scenarios. Compared with a single-radar tracker, the position, velocity, size, and direction estimation errors of the track fusion tracker are reduced by 85.5%, 64.6%, 75.3%, and 9.5% respectively, and the average value of GOSPA indicators is reduced by 19.8%; more accurate target state information can be obtained than a single-radar tracker.

Enhanced Technique for Prediction of Z-R Relationship in Tropical Region

Over the years, research has been directed to investigate radar as potential tools for estimating rain. The radar reflectivity is measured by comparing the location of the rain gauge and the associated vertical path at certain altitude. The latitude and longitude of the rain gauge is identified to pinpoint the exact associated location by using Rainbow®5 meteorological software. In this work, radar reflectivity was estimated by using S-band Terminal Doppler Weather Radar (TWDR) data at Malaysian KLIA radar station. The rain gauge is owned by Malaysian Department of Irrigation and Drainage (DID) at several locations within the vicinity of the radar. A technique to estimate the Z-R relationship by using radar reflectivity information and rainfall rate from rain gauge values is demonstrated in this article. Comparisons between radar reflectivity and actual rain gauge measurement is presented. Preliminary findings using available Z-R relation shows similar values with the actual measurement and suitable to be used in tropical climate country such as Malaysia.

Soil Moisture Retrieval in Bare Agricultural Areas Using Sentinel-1 Images

Soil moisture maps are essential for hydrological, agricultural and risk assessment applications. To best meet these requirements, it is essential to develop soil moisture products at high spatial resolution, which is now made possible using the free Sentinel-1 (S1) SAR (Synthetic Aperture Radar) data. Some soil moisture retrieval techniques using S1 data relied on the use of a priori weather information in order to increase the precision of soil moisture estimates, which required access to a weather-forecasting framework. This paper presents an improved and fully autonomous solution for high-resolution soil moisture mapping in bare agricultural areas. The proposed solution derives a priori weather information directly from the original Sentinel images, thus bypassing the need for a weather forecasting framework. For soil moisture estimation, the neural network technique was implemented to ensure the optimum integration of radar information. The neural networks were trained using synthetic data generated by the modified Integral Equation Model (IEM) model and validated on real data from two study sites in France and Tunisia. The main findings showed that the use of a radar signal averaged over grids of a few km2 in addition to radar signal at plot scale instead of a priori weather information provides good soil moisture estimations. The accuracy is even slightly better compared to the accuracy obtained using a priori weather information.