Radar Data Research Articles

Leveraging Neutrosophic TOPSIS with Artificial Intelligence-Driven Tropical Cyclone Intensity Estimation for Weather Prediction

Tropical cyclones (TCs) are powerful, low-pressure weather systems attributed to heavy rainfall and strong winds, and have often resulted in extensive damage to coastal regions. TC intensity prediction, an essential aspect of meteorological forecasting, includes evaluating the strength of the storm to facilitate disaster preparedness and alleviate possible risks. Classical approaches for the prediction of TC intensity rely on different oceanic and atmospheric parameters, but the incorporation of artificial intelligence (AI) approaches, especially those leveraging image data, provides positive breakthroughs in efficiency and accuracy. By harnessing AI techniques like deep learning architectures and convolutional neural networks (CNNs), meteorologists could analyze radar data, satellite imagery, and other visual inputs to distinguish complicated patterns indicative of intensity changes and TC development. This combination of weather science and AI-driven image analysis enables more timely and precise predictions and improves our understanding of TC dynamics, eventually fortifying protection against the impacts of formidable storms. This article introduces Neutrosophic TOPSIS with Artificial Intelligence Driven Tropical Cyclone Intensity Estimation (NTOPSIS-TCIE) technique for Weather Prediction. The presented NTOPSIS-TCIE technique determines the intensities of the TC which in turn helps to forecast weather. In the NTOPSIS-TCIE technique, median filtering (MF) approach is used to remove the noise in the images. In addition, the features are extracted using deep convolutional neural network (CNN) model. To enhance the performance of the CNN model, Harris Hawks Optimization (HHO) algorithm is applied. Finally, the NTOPSIS model is employed for the prediction of TC intensities. The performance of the NTOPSIS-TCIE technique can be studied using TC image dataset and the results signify its promising results over other models

Remote Gait Analysis Using Ultra-Wideband Radar Technology Based on Joint Range-Doppler-Time Representation.

In recent years, radar technology has been extensively utilized in contactless human behavior monitoring systems. The unique capabilities of ultra-wideband (UWB) radars compared to conventional radar technologies, due to time-of-flight measurements, present new untapped opportunities for in-depth monitoring of human movement during overground locomotion. This study aims to investigate the deployability of UWB radars in accurately capturing the gait patterns of healthy individuals with no known walking impairments. A novel algorithm was developed that can extract ten clinical spatiotemporal gait features using the Doppler information captured from three monostatic UWB radar sensors during a 6-meter walking task. Key gait events are detected from lower-extremity movements based on the joint range-Doppler-time representation of recorded radar data. The estimated gait parameters were validated against a gold-standard optical motion tracking system using 12 healthy volunteers. On average, nine gait parameters can be consistently estimated with 90-98% accuracy, while capturing 94.5% of participants' gait variability and 90.8% of inter-limb symmetry. Correlation and Bland-Altman analysis revealed a strong correlation between radar-based parameters and the ground-truth values, with average discrepancies consistently close to 0. Results prove that radar sensing can provide accurate biomarkers to supplement clinical human gait assessment, with quality similar to gold standard assessment. Radars can potentially allow a transition from expensive and cumbersome lab-based gait analysis tools toward a completely unobtrusive and affordable solution for in-home deployment, enabling continuous long-term monitoring of individuals for research and healthcare applications.

Global Soil Salinity Estimation at 10 m Using Multi-Source Remote Sensing

Salinization is a threat to global agricultural and soil resource allocation. Current investigations of global soil salinity are limited to coarse spatial resolution of the available datasets (>250 m) and semiqualitative classification rules (five ranks). Based on these two limitations, we proposed a framework to quantitatively estimate global soil salt content in five climate regions at 10 m by integrating Sentinel-1/2 remotely sensed images, climate, parent material, terrain data, and machine learning. In hyper-arid and arid region, models established using Sentinel-2 and other geospatial data showed the highest accuracy with R 2 of 0.85 and 0.62, respectively. In semi-arid, dry sub-humid, and humid regions, models performed best using Sentinel-1, Sentinel-2, and other geospatial data with R 2 of 0.87, 0.80, and 0.87, respectively. The accuracy of the global models is considerable with field validation in Iran and Xinjiang, and compared with digitized salinity maps in California, Brazil, Turkey, South Africa, and Shandong. The proportion of extremely saline soils in Europe is 10.21%, followed by South America (5.91%), Oceania (5.80%), North America (4.05%), Asia (1.19%), and Africa (1.11%). Climatic conditions, groundwater, and salinity index are key covariates in global soil salinity estimation. Use of radar data improves estimation accuracy in wet regions. The map of global soil salinity at 10 m provides a detailed, high-precision basis for soil property investigation and resource management.

CoIR: Compressive Implicit Radar.

Using millimeter wave (mmWave) signals for imaging has an important advantage in that they can penetrate through poor environmental conditions such as fog, dust, and smoke that severely degrade optical-based imaging systems. However, mmWave radars, contrary to cameras and LiDARs, suffer from low angular resolution because of small physical apertures and conventional signal processing techniques. Sparse radar imaging, on the other hand, can increase the aperture size while minimizing the power consumption and read out bandwidth. This paper presents CoIR, an analysis by synthesis method that leverages the implicit neural network bias in convolutional decoders and compressed sensing to perform high accuracy sparse radar imaging. The proposed system is data set-agnostic and does not require any auxiliary sensors for training or testing. We introduce a sparse array design that allows for a 5.5× reduction in the number of antenna elements needed compared to conventional MIMO array designs. We demonstrate our system's improved imaging performance over standard mmWave radars and other competitive untrained methods on both simulated and experimental mmWave radar data.

Validation of Spaceborne Precipitation Radar Data by Rain Gauges and Disdrometers over the Complex Topography of the Northeastern Indian Subcontinent

Validation of Spaceborne Precipitation Radar Data by Rain Gauges and Disdrometers over the Complex Topography of the Northeastern Indian Subcontinent

Semantic-guided Depth Completion from Monocular Images and 4D Radar Data

Semantic-guided Depth Completion from Monocular Images and 4D Radar Data

Space Target Recognition Based on 4D RangeFrequency-Time-Power Radar Data Cube

Space Target Recognition Based on 4D RangeFrequency-Time-Power Radar Data Cube

Spatial Evaluation of Estimation Rainfall on Weather Radar Using Marshall-Palmer Z-R Method in West Java

Spatial Evaluation of Estimation Rainfall on Weather Radar Using Marshall-Palmer Z-R Method in West Java

Improved Orthorectification and Empirical Reduction of Topographic Effects in Monostatic Mini-RF S-band Observations of the Moon

The Miniature Radio Frequency instrument (Mini-RF) on the Lunar Reconnaissance Orbiter obtained widespread synthetic aperture radar observations of the Moon in the S band (12.6 cm), including nearly complete coverage at both lunar poles. The currently archived monostatic data have spatial offsets from the lunar reference frame, making them more difficult to compare to other data sets. To address this issue, we have developed a new algorithm for spatially controlling the Mini-RF S-band monostatic data set and orthorectifying these data onto lunar topography. Additionally, as the influence of incidence angle changes on radar observations is well known, we describe an empirical approach to account for variations in observation geometry and surface topography. Individual radar swaths and mosaics produced using this method more clearly show the variability in scattering behavior due to changes in lunar regolith properties and suppress some of the behavior arising from these topographic effects alone. Once these terrain effects are taken into account, we find that areas of permanent shadow at both poles have a higher median radar reflectivity than nonpermanently shadowed regions, but the polarization behavior of shadowed versus unshadowed areas is largely similar. The higher radar reflectivity in permanent shadow is likely the result of physical or compositional differences in these unique environments, though the precise cause remains uncertain. The results here illustrate how reducing the influence of topography and geometry effects in Mini-RF radar data may enable better characterization of lunar geologic units, regolith structure, and potential areas hosting volatile deposits at the lunar poles.

Application of Subset Full-Waveform Inversion to Multioffset Surface Ground-Penetrating Radar Data

Application of Subset Full-Waveform Inversion to Multioffset Surface Ground-Penetrating Radar Data

Identifying Hail Events Using Weather Radar and Satellite Data (Study Case in East Java on 23 December 2021)

Hail is an extreme weather phenomenon that rarely occurs in tropical regions such as Indonesia. However, if convective clouds are strong enough to develop higher, a hail event is likely to occur. Interestingly, on 23 December 2021, hail events occurred in four different nearby locations. Weather Radar data from the Juanda Meteorological Station and Himawari-8 Satellite data were used to analyse this hail phenomenon. The Radar measured reflectivity values, cloud top height, water vapour content, and the derivative product of the probability of hail occurrence. The Himawari-8 data were utilised to observe cloud top temperatures. From the results, it is known that the reflectivity value of hail is in the range of 45 dBZ to 55 dBZ, with a probability of hail above 80%, and the temperature at the top of the Cumulonimbus cloud is between -67.5°C to -82.5°C. These thresholds may be used in monitoring hail events, especially in the East Java region.

Proposal for Real-time Quality Control Method to Enhance Hydrological Application of Radar Rainfall

The primary aim of a rain radar is to observe ground-level rainfall accurately. Effective quality control of rain radar data is necessary to ensure accuracy. In the field of hydrology, various methods have been employed to improve the quality of radar rainfall by correcting or synthesizing it with gauge rainfall. However, it is more important to enhance the quality of radar polarimetric variables in order to improve the quality of radar rainfall. Rain radar systems generate various polarimetric variables through complex radar hardware systems and signal processing algorithms. Measurement errors may arise from equipment abnormalities, inappropriate operating settings, and obstructions in observation during signal processing algorithms and production process of polarimetric variables. Because rain radar is an observation device operating in real time, it entails more frequent and continuous quality control compared to flowmeters that intermittently measure streamflow. This study introduces a series of real-time quality control methods tailored for rain radar, including inspection of radar rainfall accuracy, quality analysis of polarimetric variables (ρ<sub>HV</sub>, Z<sub>DR</sub>, Φ<sub>DP</sub>), error estimation of polarimetric variables, and long-term variability of errors.

Application of low-altitude wind shear recognition algorithm and laser wind radar in aviation meteorological services

Abstract The rapid development of the aviation industry has attracted great attention to aviation safety, and the identification and early warning of low-level wind shear have become crucial. Therefore, it is also necessary to improve the performance of laser wind radar technology and achieve accurate recognition of low-level wind shear. In this regard, this study is based on laser wind radar technology and uses the K-neighborhood frequency method to denoise radar data. And based on the least squares fitting method, two-dimensional synthetic wind shear is obtained, and a low altitude wind shear recognition model based on laser wind radar is constructed. These test results confirm that the correlation coefficient values of the wind speed fitting curve measured by the laser detection radar on sunny, cloudy, and fog days are 0.978, 0.954, and 0.961, respectively. And the correlation coefficient values of wind direction fitting curve are 0.984, 0.982, and 0.952, respectively. On sunny, cloudy, and fog days, the recognition accuracy values of the low-level wind shear recognition model are 85.92, 82.17, and 72.43%, respectively. And the recognition accuracy values of wind analyzer radar are 70.18, 62.57, and 55.89%, respectively. In practical applications, the low-level wind shear recognition model accurately recognizes the occurrence of low-level wind shear. This study has achieved accurate recognition of low-level wind shear and has better performance compared to other existing radars. It has played an important reference role in aviation safety.

IMPLEMENTATION OF GABELLA METHOD AND RANDOM FOREST FOR GROUND CLUTTER DETECTION IN PADANG WEATHER RADAR DATA

Weather radar is an active remote sensing instrument for various hydrological and meteorological applications. One advantage of weather radar is its ability to detect rainfall in space and time with high spatial resolution. However, one of the issues that contaminate radar observations is ground clutter. Ground clutter is a signal or echo from non-meteorological objects on the earth’s surface that are stationary in the time domain. Detecting and mitigating clutter effects is crucial to achieve precise weather measurements. This research aims to implement the Gabella and random forest methods to detect ground clutter in Padang weather radar data and determine the optimal method between the two. The implementation of the Gabella method for detecting ground clutter in Padang weather radar data was suboptimal. This was due to the most duplicated data at the same point being only 15.97% of the total data. Meanwhile the random forest method obtained a kappa value of 92.03%. This indicates that the random forest model created using 2000 trees as the parameter performs well. Based on these results, the random forest method identified as the most optimal approach for detecting ground clutter in Padang weather radar data.

GCNet: Ground Collapse Prediction Based on the Ground-Penetrating Radar and Deep Learning Technique

Manual methods to detect and identify potential ground collapse hazards have a high volume of workload and rely on manual skills and experience strongly, with substantial inconsistencies. This paper proposes an automatic recognition method of potential underground hazards and designs an object detection-based algorithm to predict the hidden danger of ground collapse, called GCNet. The GCNet is a ground collapse hazard detection network, which is developed to detect underground hazards, including cavities, poor soil quality, pipelines, and soil background. The GCNet uses the deep residual network ResNet101 and feature pyramid network (FPN) to extract features and a task coordination network (TCN) to identify the category and location of hidden underground hazards. Further, a feature enhancement method based on the regional binary pattern is proposed to improve the accuracy of the proposed model by expanding the ground-penetrating radar (GPR) data by adding multi-level features to GPR images. The experiments are conducted on a large amount of real GPR data and experiment results show that the proposed automatic recognition method can surpass the existing deep learning-based methods in hidden underground hazard recognition and identification.

A New Vector for Mapping Gold Mineralization Potential and Proposed Pathways in Highly Weathered Basement Rocks using Multispectral, Radar, and Magnetic Data in Random Forest Algorithm

A New Vector for Mapping Gold Mineralization Potential and Proposed Pathways in Highly Weathered Basement Rocks using Multispectral, Radar, and Magnetic Data in Random Forest Algorithm

Temporal development of the displacement field of the Ponzano landslide in February 2017

The conducted research determined the temporal evolution of the displacement field for the Ponzano landslide case study. The offset-tracking method, so far used mainly for the relatively rapid but uniform displacement of glaciers, was tested for the 2017 study of the Ponzano landslide in Italy. The suitability of the method for high-resolution TerraSAR-X and medium-resolution Sentinel-1 imagery was investigated. The results proved the applicability of the OT method for studying processes with high and variable displacement dynamics. However, for such purposes, high-resolution radar data are crucial. With an uncertainty in the determination of residual displacements of about ±1 m, it was shown that the values of residual displacements occurring up to several days after the main phase of landslide movements are within the range of uncertainty but are determinable. The research conducted in the paper filled a gap in the analysis of the phenomenon just after the main movement phase. It allowed determination of the time and speed of extinction of landslide movements.

A framework for analyzing energy consumption in urban built-up areas based on single photonic radar and spatial big data

To achieve carbon neutrality quickly and deal with urban greenhouse effect, it is necessary to study the energy consumption of urban buildings. Energy consumption prediction is an important step in planning and managing energy use in buildings. At present, few studies have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings in urban built-up areas. In this paper, a spatial data-driven framework is introduced for the analysis and assessment of building energy consumption. The framework is grounded in the building centroid and is combined with single photon radar and point of interest data through the utilization of the nearest neighbor classification algorithm. Two megacities, Beijing and Shanghai, are taken as examples to illustrate the method and to generate the regional building energy use database. The results show that: a) building physical feature (geometric form, functional type) have a significant impact on energy consumption, and energy consumption is correlated with the economic level of the region; b) Energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphon effect is obvious in region with high energy consumption. The contribution of the framework is to provide insights into the feasibility of employing multiple spatial data fusion for the analysis of energy consumption within urban built-up areas. This can be employed to address energy planning concerns and help promote sustainable environmental practices in urban areas worldwide.

Radio Frequency Interference Mitigation in Synthetic Aperture Radar Data Based on Instantaneous Spectrum Forward Consecutive Mean Excision

Radio frequency interference (RFI) poses major threats to synthetic aperture radar (SAR) systems. Due to the suppression of useful target signals via high-power RFI, the SAR imaging quality is severely degraded. Nevertheless, existing studies on RFI mitigation mainly focus on narrowband filtering, while wideband RFI mitigation methods are relatively lacking and perform non-robustly. In this paper, an RFI mitigation scheme is proposed based on instantaneous spectrum forward consecutive mean excision (FCME), which is suitable for both narrowband and wideband RFI mitigation. The SAR echo signal is first transformed into a time–frequency (TF) domain through a short-time Fourier transform (STFT). On this basis, the instantaneous spectra polluted via RFI are detected via a kurtosis-based statistical test and then filtered via FCME to achieve RFI mitigation. Finally, connected component analysis is applied as a safety measure so as to avoid the unnecessary loss of useful target signal. The combination of FCME and connected component analysis enables the proposed method to thoroughly filter out RFI while retaining more useful target signals compared with other competing methods. The experimental results on real SAR raw data validate the effectiveness of the proposed method.

Overview of Circular Structures of Various Origins and Sizes in Egypt as a Contribution to Natural Hazard Data Mining Based on Remote Sensing Data and Geoinformation Systems (GIS) Analysis

This study is focused on the detection and typification of circular features with different sizes, origins, and states of erosion as well as on their surrounding tectonic setting, as well as on their impact on the environment and on the occurrence of natural hazards based on different satellite data of Egypt. Sentinel 2, Landsat and ASTER images and Sentinel 1- and ALOS L-band Phased Array Synthetic Aperture Radar (PALSAR)-radar data make it possible to identify larger ring structures as well as smaller circular features like maars or sinkholes in karst areas. Evaluations of the various satellite data contribute to the systematic and standardized inventory ring structures, most of which are related to magmatic intrusions. Such an inventory is a prerequisite for hazard preparedness and should be part of natural hazard data mining integrated into a Geo Information System (GIS). Mapping traces of volcanic activities (craters, maars, cones) is essential as a contribution to land use planning. By gathering the data and integrating the knowledge of the different ring structures in standardized GIS data base one of the many steps towards hazard preparedness and adapted land use planning can be achieved. Circular features are often buried by aeolian and fluvial sediments and become only visible on radar images or on Landsat or ASTER RGB images combining thermal bands. Larger ring structures have not only an influence on groundwater flow but also on geodynamic activity (earthquakes and related secondary effects).