Radar Data Research Articles

Improved Polar Current Shell Algorithm for Ocean Current Retrieval from X-Band Radar Data

This paper presents an improved algorithm for retrieving ocean surface currents from X-band marine radar images. The original polar current shell (PCS) method begins with a 3D fast Fourier transform (FFT) of the radar image sequence, followed by the extraction of the dispersion shell from the 3D image spectrum, which is then transformed into a PCS using polar coordinates. Building on this foundation, the improved approach is to analyze all data points corresponding to different wavenumber magnitudes in the PCS domain rather than analyzing each specific wavenumber magnitude separately. In addition, kernel density estimation (KDE) to identify high-density directions, interquartile range filtering to remove outliers, and symmetry-based filtering to further reduce noise by comparing data from opposite directions are also utilized for further improvement. Finally, a single curve fitting is applied to the filtered data rather than conducting multiple curve fittings as in the original method. The algorithm is validated using simulated data and real radar data from both the Decca radar, established in 2008, and the Koden radar, established in 2017. For the 2008 Decca radar data, the improved PCS method reduced the root-mean-square deviation (RMSD) for speed estimation by 0.06 m/s and for direction estimation by 3.8° while improving the correlation coefficients (CCs) for current speed by 0.06 and direction by 0.07 compared to the original PCS method. For the 2017 Koden radar data, the improved PCS method reduced the RMSD for speed by 0.02 m/s and for direction by 4.6°, with CCs being improved for current speed by 0.03 and direction by 0.05 compared to the original PCS method.

Ballistic Fitting Impact Point Prediction Based on Improved Crayfish Optimization Algorithm

To solve the problem of difficulty in predicting the impact point clearly and promptly during projectile flight, this paper proposes an improved ballistic-impact-point prediction method. A certain type of high-spinning tailed projectile is taken as the research object for online real-time landing point prediction research. This study comprehensively utilizes the real-time radar measurement data and the geomagnetic data measured by the bomb-carried geomagnetic sensor. It applies the four-degree-of-freedom ballistic model to predict the landing point. First, the roll angular velocity is calculated based on the geomagnetic data, after which the radar real-time measurement data are segmentally fitted using the improved crayfish algorithm. Then, the fitted parameters are substituted into the four-degree-of-freedom ballistic model. Finally, the C-K method is used to identify the aerodynamic parameters, and the identified aerodynamic parameters are used for fallout prediction. The simulation results show a small deviation between the predicted and actual impact points using the improved ballistic-impact-point prediction method.

Automatic detection of water pipeline leakage via unsupervised learning applied on ground penetrating radar data

ABSTRACT Aging urban water supply pipelines are facing significant leakage issues. Ground penetrating radar (GPR) is an effective non-destructive method for leakage localization, but its image analysis mainly relies on interpreter experience, leading to uncertainty and inefficiency. To address this issue, an unsupervised learning method based on GPR data attributes is proposed for automatic leakage detection. First, velocity and energy attributes are extracted from B-Scan profiles to differentiate wet areas caused by leakage from normal areas. Then, the density-based spatial clustering of applications with noise (DBSCAN) clustering method is applied to automatically classify these two types of areas. The proposed method was evaluated on an experimental platform with constant water pressure and pre-drilled leakage points. Experiments show that the velocity and energy attributes decrease by 25.2 and 26.6% after leakage. Using velocity attributes yields better results, and suggestions for DBSCAN hyperparameters are provided. Feature importance analysis indicates that two-way-travel-time with a score of 46.1 and energy attribution index with a score of 32.7 significantly influence leakage identification. This method can also estimate affected leakage areas with an error not exceeding the spacing of three measurement lines, and is available across various underground conditions without needing well-labeled training data.

Mapping Soil Surface Moisture of an Agrophytocenosis via a Neural Network Based on Synchronized Radar and Multispectral Optoelectronic Data of SENTINEL-1,2—Case Study on Test Sites in the Lower Volga Region

In this article, the authors developed a novel method for the moisture mapping of the soil surface of agrophytocenosis using a neural network based on synchronized radar and multispectral optoelectronic data from Sentinel-1,2. The significance of this research lies in its potential to enhance precision farming practices, which are increasingly vital in addressing global agricultural challenges such as water scarcity and the need for sustainable resource management. To verify the developed method, data from two experimental plots were utilized. These plots were located on irrigated soybean crops, with the first plot situated on the right bank (plot No. 1) and the second on the left bank (plot No. 2) of the lower Volga River. Two experimental soil moisture geodatasets were created through measurements and geo-referencing points using the gravimetric method (for plot No. 1) and the proximal sensing method (for plot No. 2) employing the Soil Moisture Sensor ML3-KIT (THETAKIT, Delta). The soil moisture retrieval algorithm was based on the use of a neural network to predict the reflection coefficient of an electro-magnetic wave from the soil surface, followed by inversion into soil moisture using a dielectric model that takes into account the soil texture. The input parameter of the neural network was the ratio of the microwave radar vegetation index (calculated based on Sentinel-1 data) to the index (calculated based on the data of multispectral optoelectronic channels 8 and 11 of Sentinel-2). The retrieved soil moisture values were compared with in situ measurements, showing a determination coefficient of 0.44–0.65 and a standard deviation of 2.4–4.2% for plot No. 1 and similar metrics for plot No. 2. The conducted research laid the groundwork for developing a new technology for remote sensing of soil moisture content in agrophytocenosis, serving as a crucial component of precision farming systems and agroecology. The integration of this technology promotes sustainable agricultural practices by minimizing water consumption while maximizing crop productivity. This aligns with broader environmental goals of conserving natural resources and reducing agricultural runoff. On a larger scale, data derived from such studies can inform policy decisions related to water resource management, guiding regulations that promote efficient water use in agriculture.

Enhancing Forest Canopy Height Mapping in Kaziranga National Park, Assam, by Integrating LISS IV and SAR data with GEDI LiDAR data Using Machine Learning

Abstract. This study investigates the integration of spaceborne Global Ecosystem Dynamics Investigation (GEDI) LiDAR data with optical imagery from Linear Imaging Self Scanning Sensor (LISS IV), Sentinel-1 Synthetic Aperture Radar (SAR) and PALSAR data for continuous forest canopy height mapping in Kaziranga National Park (KNP), Assam for the years 2018 and 2022. Four machine learning models were trained and evaluated to assess the predictive ability of LISS IV data in conjunction with SAR variables. The mean canopy height was measured at approximately 8.58 m in 2018, which increased to about 9.07m in 2022. Results reveal Extreme Gradient Boosting (XGB) as the top-performing model, achieving an RMSE of 5.47m and an R2 of 0.55. In comparison, Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbors (kNN) achieved RMSE values of 5.49, 5.51, and 5.73, respectively. Analysis shows a prevalent occurrence of canopy heights below 5 meters in KNP (more than 35% of the area), while taller canopies beyond 20m can be found in less than 5% of the area. This finding underscores the importance of integrating satellite data and machine learning and highlights the novel application of LISS IV data in enhancing canopy height mapping. Furthermore, it represents the first comprehensive attempt to map canopy height in KNP, laying the groundwork for further research on biomass assessment and carbon sequestration in this vital biodiversity hotspot. Overall, the study highlights the potential of leveraging advanced remote sensing technologies and machine learning approaches for improved understanding and management of forest ecosystems.

Deep network based approaches to mitigate seasonal effects in SAR images for deforestation monitoring

Abstract. Most deforestation monitoring programs are based on optical images, which are severely affected by clouds, especially in tropical regions. As an alternative, Synthetic Aperture Radar (SAR) data are minimally affected by atmospheric conditions. However, one of the challenges inherent in such approaches is the effect of seasonal rainforest variations over the SAR images. Recently proposed stabilization algorithms mitigate this effect with significant accuracy gains, however, at the cost of higher computational resources, needed to accommodate long temporal SAR image sequences. This work addresses this issue and presents two alternative solutions that attain similar or better accuracy at a much lower computational requirements. One solution is based on a ResUnet, while the second combines an LSTM and a UNet. Both approaches were tested using raw and stabilized pairs as well as raw sequences. Experiments have indicated that both approaches can mitigate the seasonality effect using a much shorter SAR image sequences than modern stabilization methods require. The results with the multitemporal input outperformed those with the preprocessed bitemporal set by 4.3% in Recall, 1.7% in Precision, and 2.7% in F1-Score, also delivering the best deforestation probability maps.

Terrain analysis in forested areas: consequences of using DSM instead of DTM

Abstract. Relief mapping through dense tropical forest is a challenge, which can be met by processing radar images. Various global digital elevation models (DEMs) are available, either free or paid, the most widely used methods for producing them at global scale being photogrammetry and short wavelength radar interferometry. However, the resulting DEMs do not directly represent the ground surface but rather the canopy or something in between. Since most users need terrain models for a variety of applications in geoscience, water management etc. but use such canopy models for lack of anything better, it was relevant to assess the consequences of this inappropriate but unavoidable choice. This paper presents a study carried out in the Brazilian Amazon, where an airborne P-band radar interferometric DEM was used as a reference based on its accuracy assessed in previous studies. A well-established DEM (SRTM) and newer ones (Copernicus and Fabdem), as well as a DEM obtained from aerial X-bands radar data, were compared to the reference according to different criteria related to elevation (accuracy and a typical application, i.e., dam filling simulation) and to slope at different scales (accuracy and a typical application, i.e., terrain classification). The results highlighted the risks of using short wavelengths to represent the terrain and emphasized the importance of slope in different resolution scales.

Monitoring Aeolian Erosion from Surface Coal Mines in the Mongolian Gobi Using InSAR Time Series Analysis

Surface mining in the southeastern Gobi Desert has significant environmental impacts, primarily due to the creation of large coal piles that are highly susceptible to aeolian processes. Using spaceborne remote sensing and numerical simulations, we investigated erosional processes and their environmental impacts. Our primary tool was Interferometric Synthetic Aperture Radar (InSAR) data from Sentinel-1 imagery collected between 2017 and 2022. We analyzed these data using phase angle information from the Small Baseline InSAR time series framework. The time series analyses revealed intensive aeolian erosion in the coal piles, represented as thin deformation patterns along the potential pathways of aerodynamic transportation. Further analysis of multispectral data, combined with correlations between wind patterns and trajectory simulations, highlighted the detrimental impact of coal dust on the surrounding environment and the mechanism of aeolian erosion. The lack of mitigation measures, such as water spray, appeared to exacerbate erosion and dust generation. This study demonstrates the feasibility of using publicly available remote sensing data to monitor coal mining activities and their environmental hazards. Our findings contribute to a better understanding of coal dust generation processes in surface mining operations as well as the aeolian erosion mechanism in desert environments.

Observed Variability in Convective Cell Characteristics and Near-Storm Environments across the Sea- and Bay-Breeze Fronts in Southeast Texas

Abstract During the DOE Atmospheric Radiation Measurement (ARM) Tracking Aerosol Convection Interactions Experiment (TRACER) IOP spanning June–September 2022, two fixed ARM sites and a mobile team concurrently sampled the airmass heterogeneity across sea- and bay-breeze fronts around the greater Houston metropolitan region. Here, we quantify the spatiotemporal variability between maritime (coastal/bay side of breeze fronts) and continental (inland side of breeze fronts) air masses over 15 IOP days characterized by strong sea-breeze forcing. We analyze environmental profile data from 177 radiosondes and use S- and C-band radar data to track and quantify the variability in attributes of more than 2300 shallow and transitioning cells across different air masses. The composite analysis of environmental profiles indicates that during the early afternoon, the sea-breeze maritime air mass exhibits lower convective available potential energy (CAPE) than the bay-breeze maritime air mass. As the sea breeze advances inland with time, CAPE within the maritime air mass exceeds that of the continental air mass to the north of the breeze fronts. In general, maritime cells have a larger mean composite reflectivity and cell widths than continental cells; however, the response varies between shallow and transitioning cells. Mean composite 20-dBZ echo-top heights, however, are similar across air masses for both shallow and transitioning cells. The continental and maritime inflow air mass for transitioning cells has significantly different mean values for mixed-layer entrainment CAPE, lifted condensation level, level of free condensation, boundary layer depth, and diluted equilibrium level. For shallow cells, only total precipitable water shows a significant difference. Significance Statement The greater Houston metropolitan area is a natural laboratory for understanding the individual impacts of background meteorology and aerosols on convective clouds. Due to its proximity to the Gulf Coast and Galveston Bay, the Houston region experiences a diurnal precipitation cycle in the summer, driven by convection triggered from sea- and bay-breeze fronts. These fronts act as a boundary between air masses with distinct thermodynamic and environmental characteristics. Convergence along these fronts and interactions between storm outflow and the fronts facilitate convection initiation in different mesoscale air masses. This study quantifies the heterogeneity among these air masses while investigating their influence on cloud microphysics. We find that the effect of airmass heterogeneity is more pronounced for the bulk microphysical properties in shallow clouds.

Using Microwave Simulations for the Generation of Radar Data for Gesture and Activity Recognition: Potential and Challenges for Real-World Applications

Using Microwave Simulations for the Generation of Radar Data for Gesture and Activity Recognition: Potential and Challenges for Real-World Applications

Intercomparison of Surface Currents Obtained Using SCHISM and the HF Radar Data in Galveston Bay and Sabine Lake, Texas

Intercomparison of Surface Currents Obtained Using SCHISM and the HF Radar Data in Galveston Bay and Sabine Lake, Texas

Modeling aboveground carbon in flooded forests using synthetic aperture radar data: a case study from a natural reserve in Turkish Thrace

Modeling aboveground carbon in flooded forests using synthetic aperture radar data: a case study from a natural reserve in Turkish Thrace

SmokeNav: Millimeter‐Wave‐Radar/Inertial Measurement Unit Integrated Positioning and Semantic Mapping in Visually Degraded Environments for First Responders

First responders often face hazardous and life‐threatening situations in environments filled with smoke, posing significant risks to their safety. The existing perception solutions, such as camera or light detection and ranging (LiDAR)‐based methods, are inadequate when faced with visually degraded conditions caused by smoke. In this work, SmokeNav, a novel system that combines data from an inertial sensor and millimeter‐wave (mmWave) radar, is proposed to enhance situational awareness for first responders in smoky environments. SmokeNav utilizes an inertial positioning module that exploits the human motion constraints with a foot‐mounted inertial measurement unit to provide accurate user localization. By integrating this location information with mmWave radar data, it employs a probabilistic occupancy map construction to reconstruct an accurate metric map. To enable semantic understanding of the environment, a DNN‐based semantic segmentation model that incorporates radar reflectivity and employs focal loss to improve performance is introduced. Herein, extensive real‐world experiments in smoky environments is conducted to demonstrate that SmokeNav precisely localizes the user and generates detailed maps with semantic segmentation. In this work, potentials are held for enhancing the safety and effectiveness of first responders in hazardous conditions.

What Is Beyond Hyperbola Detection and Characterization in Ground-Penetrating Radar Data?—Implications from the Archaeological Site of Goting, Germany

What Is Beyond Hyperbola Detection and Characterization in Ground-Penetrating Radar Data?—Implications from the Archaeological Site of Goting, Germany

Removing cloudiness on optical space images by a generative adversarial network model using SAR images

The object of this study is the process of removing cloudiness on optical space images. Solving the cloudiness removal task is an important stage in processing data from the Earth remote probing (ERP) aimed at reconstructing the information hidden by these atmospheric disturbances. The analyzed shortcomings in the fusion of purely optical data led to the conclusion that the best solution to the cloudiness removal problem is a combination of optical and radar data. Compared to conventional methods of image processing, neural networks could provide more efficient and better performance indicators due to the ability to adapt to different conditions and types of images. As a result, a generative adversarial network (GAN) model with cyclic-sequential 7-ResNeXt block architecture was constructed for cloud removal in optical space imagery using synthetic aperture radar (SAR) imagery. The model built generates fewer artifacts when transforming the image compared to other models that process multi-temporal images. The experimental results on the SEN12MS-CR data set demonstrate the ability of the constructed model to remove dense clouds from simultaneous Sentinel-2 space images. This is confirmed by the pixel reconstruction of all multispectral channels with an average RMSE value of 2.4 %. To increase the informativeness of the neural network during model training, a SAR image with a C-band signal is used, which has a longer wavelength and thereby provides medium-resolution data about the geometric structure of the Earth's surface. Applying this model could make it possible to improve the situational awareness at all levels of control over the Armed Forces (AF) of Ukraine through the use of current space observations of the Earth from various ERP systems

Early Season Crop Acreage Estimation for Pigeon Pea using SAR Data: A Case Study of Kalaburagi District, Karnataka

Crop acreage estimates using space-based observations during mid-season and pre-harvest periods are operationally provided for major Kharif and Rabi crops in India. These are critical inputs for planning and decision-making concerning food security, storage, pricing, and procurement. This work explores the potential of Satellite C-band Synthetic Aperture Radar (SAR) data in VV and VH polarization, acquired during monsoon season, for early crop acreage assessment of Pigeon Pea. The present study evaluates two classification approaches based on multi-temporal SAR data to generate a spatial distribution map of the pigeon pea crop in the Kalaburagi region of Karnataka. The temporal profiles of sigma nought (backscatter coefficient) and Entropy vs. Alpha Angle were studied with reference to crop phenology. Random forest algorithm has been used to classify multi-temporal SAR Ground Range Detected (GRD) data from June to November 2019. Temporal SAR data from June to early November with VV+VH polarization was optimal for tur area assessment with 85% accuracy. This study also examines phase and intensity information from 3-date SAR Single Look Complex (SLC) data with an unsupervised Wishart Classification approach for improved crop acreage estimation. The results emphasize the effectiveness of dual-polarimetric SAR data for timely crop inventory of Pigeon Pea during the Kharif season.

Comparative analysis of hybrid cognitive radar techniques for enhanced target detection and tracking: A performance evaluation perspective

This paper introduces an innovative hybrid algorithmic approach for cognitive radar systems, by integrating unique machine learning optimization techniques such as, YOLO (You Only Look Once), Mask R-CNN, and Recurrent Neural Networks (RNNs). Through extensive simulations, the integrated approach demonstrates notable enhancements in target detection, instance segmentation, and target tracking within radar systems. Leveraging deep learning models, the framework facilitates adaptive and intelligent processing of radar data, augmenting system performance in dynamic environments. By seamlessly integrating state-of-the-art techniques, the proposed framework showcases a comprehensive solution to the challenges faced by traditional radar systems. This research represents a significant stride forward in radar technology, promising transformative impacts across various domains, including surveillance, remote sensing, and autonomous navigation. As radar systems continue to evolve, the adoption of advanced deep learning techniques offers unprecedented opportunities for enhancing situational awareness and decision-making capabilities in complex operational scenarios.

Analysing Sector Safety Performance Based on Potential Conflicts: Complex Network Approach

This paper presents an in-depth analysis of Sector Safety Performance (SeSPe), focusing on potential conflicts within the Spanish Air Traffic Network. SeSPe serves as a comprehensive analysis for evaluating the safety levels of individual airspace sectors, taking into account both the frequency and severity of potential conflicts, along with their geospatial characteristics. Using Complex Network Theory, this study applies a novel methodology to four Madrid ACC sectors, analysing one month of flight track data. Multiple weighted spatial-temporal networks are built using 60 min intervals, allowing for the identification of critical nodes and the examination of interconnections and structural dynamics within the air traffic network. By incorporating temporal variations, the analysis uncovers evolving patterns. The research introduces an innovative approach for calculating potential conflicts by integrating radar data with airspace structure, thus offering a deeper understanding of sector risk profiles. The development of the SeSPe indicator, which combines historical safety data with topological features, enables a more informed and strategic evaluation of Air Traffic Management (ATM) system performance, ultimately contributing to safer and more efficient airspace operations.

Accelerating Deep Learning in Radar Systems: A Simulation Framework for 60 GHz Indoor Radar

FMCW radar systems are increasingly used in diverse applications, and emerging technologies like JCAS offer new opportunities. However, machine learning for radar faces challenges due to limited application-specific datasets, often requiring advanced simulations to supplement real-world data. This paper presents a setup for generating synthetic radar data for indoor environments, evaluated using CNNs. The setup involves comprehensive modeling, including far-field antenna simulations, variations in human radar cross-section, and detailed representations of indoor environments with their corresponding propagation channel properties. These synthetic data are used to train CNNs, and their performance is assessed on real measurement data. The results demonstrate that CNNs trained on synthetic data can perform well when tested on real measurement data. Specifically, the models trained with synthetic data showed performance comparable to models trained with real measurement data, which required a minimum of 300 samples to reach similar levels of accuracy. This result demonstrates that synthetic data can effectively train neural networks, providing an alternative to real measurement data, particularly when collecting sufficient real-world samples is difficult or costly. This approach significantly reduces the time required for generating datasets, and the ability to quickly label data in simulations simplifies and accelerates post-processing. Additionally, the generated datasets can be made more heterogeneous by introducing varying signal conditions, enhancing the diversity and robustness of the training data.

BATS: Bat‐aggregated time series—A python‐based toolkit for landscape‐level monitoring of free‐tailed bats via weather radar

Abstract The US operates a system of 160 S‐band Doppler weather radars known as NEXRAD (NEXt generation weather RADar) that continuously monitors the airspace around the majority of the United States and outlying territories. These radars detect and track birds, insects, and bats. Free‐tailed bats (Genus Tadarida) provide considerable ecosystem services through their voracious insect consumption; but their movements and ecosystem service provision have historically been difficult to track/study in space and time. We introduce ‘BATS’, a Python toolkit that streamlines the process of downloading, classifying, and aggregating time series of free‐tailed bats across large landscapes. BATS retrieves data from NOAA's weather radar data repositories and classifies the processed radar data using a pre‐trained ML trained to detect and classify radar echoes associated with free‐tailed bats. We trained various machine learning approaches at classifying pixels containing free‐tailed bats and compared the effectiveness across approaches. With an AUC of 0.963, the neural network approach is highly effective in identifying free‐tailed bats in NEXRAD data over our study sites in California and Texas. Furthermore, BATS is capable of quickly distilling 6 months of radar data from a single tower (3.5 Tb) into a single 15 Mb‐sized map of bat occurrence, contingent on available computing resources. BATS will help scientists and stakeholders identify areas of high bat occupancy at the landscape level over long periods of time. This ability has the potential to increase our understanding of the economic and agricultural value of these species.