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Aircraft icing refers to the accumulation of ice on the surface and components of an aircraft when supercooled water droplets collide with the aircraft above freezing levels (at altitudes at which the temperature is below 0 °C), which requires vigilant monitoring to avert aviation accidents attributable to icing. In response to this imperative, the Weather Radar Center (WRC) of the Korea Meteorological Administration (KMA) has developed a real-time icing detection algorithm. We utilized 3D dual-polarimetric radar variables, 3D atmospheric variables, and aircraft icing data and statistically analyzed these variables within the icing areas determined by aircraft icing data from 2018–2022. An algorithm capable of detecting icing potential areas (icing potential) was formulated by applying these characteristics. Employing this detection algorithm enabled the classification of icing potential into three stages: precipitation, icing caution, and icing warning. The algorithm was validated, demonstrating a notable performance with a probability of detection value of 0.88. The algorithm was applied to three distinct icing cases under varying environmental conditions—frontal, stratiform, and cumuliform clouds—thereby offering real-time observable icing potential across the entire Korean Peninsula. [ABSTRACT FROM AUTHOR]

In the realm of short-range radar applications, the focus on detecting "low, slow, and small" (LSS) targets has escalated, marking a pivotal aspect of critical area defense. This study pioneers the use of one-dimensional convolutional neural networks (1D-CNNs) for direct slow-time dimension radar feature extraction, sidestepping the complexity tied to frequency and wavelet domain transformations. It innovatively employs a network architecture enriched with multi-frequency multi-scale deformable convolution (MFMSDC) layers for nuanced feature extraction, integrates attention modules to foster comprehensive feature connectivity, and leverages linear operations to curtail overfitting. Through comparative evaluations and ablation studies, our methodology not only simplifies the analytic process but also demonstrates superior classification capabilities. This establishes a new benchmark for efficiently classifying low-altitude entities, such as birds and unmanned aerial vehicles (UAVs), thereby enhancing the precision and operational efficiency of radar detection systems. [ABSTRACT FROM AUTHOR]

There is growing demand for the high-fidelity characterization of satellites in Geosynchronous Earth Orbit (GEO) to support Space Domain Awareness (SDA). This is particularly true for newly launched satellites, where it is necessary for satellite providers to ascertain whether components have deployed properly. Conventional wideband radar systems are capable of imaging satellites provided that (i) they have sufficient power aperture and bandwidth, and (ii) they observe enough target aspect change to generate a resolved image. While wideband radars are used routinely for characterizing satellites in Low-Earth Orbit (LEO), powerful radars with sensitivity sufficient for large GEO ranges (36,000 km or greater) are lacking. Thus, researchers often rely on more widely available high-power narrowband tracking radars for GEO characterization. In this paper, we present a novel range-Doppler-time (RDT) tensor processing technique for GEO characterization with narrowband radar. This technique encapsulates the strengths of previously proposed methods for narrowband-radar characterization at GEO, providing a generalized approach that can be applied in a variety of settings. The technique generates fully resolved 2D images of rotating GEO satellites in low-bandwidth scenarios. In cases where aspect change is limited, the technique provides detailed Doppler information for enhanced satellite status monitoring. This work presents a comprehensive quantitative analysis of the technique that considers the impact of key parameters on characterization performance. Simulated radar data, and radar data collected in a compact range on a scaled satellite model, are used to evaluate the technique. [ABSTRACT FROM AUTHOR]

Joint transceiver beamforming is a fundamental and crucial research task in the field of signal processing. Despite extensive efforts made in recent years, the joint transceiver beamforming of frequency diverse array (FDA)-based multiple-input and multiple-output (MIMO) radar has received relatively less attention and is confronted with some tricky challenges, such as range–angle decoupling and the interaction between multiple performance metrics. In this paper, we initially derive the generalized ambiguity function of the FDA-MIMO radar to explore the intrinsic correlation between its waveform design and resolution. Following that, the joint beamforming optimization is formulated as a nonconvex bivariate quadratic programming problem (NBQP) with the aim of maximizing the Signal-to-Interference-Noise Ratio (SINR) of the FDA-MIMO radar system. Building upon this, we introduce an innovative alternating manifold optimization with nested iteration (AMO-NI) algorithm to address the NBQP. By incorporating manifold optimization into iterative updates of transmit waveform and receiving filter, the AMO-NI algorithm considers the interdependencies among the optimization variables. The algorithm efficiently and expeditiously finds global optimum solutions within a finite number of iterations. Compared with other methods, our approach yields a superior beampattern and higher SINR. [ABSTRACT FROM AUTHOR]

The proposed "Guanlan" ocean science satellite, led by China's Laoshan Laboratory, includes an interferometric radar altimeter (IRA) as a key payload. As an integral part of its development, an airborne IRA experiment was conducted on 6 November 2021, with a flight path of approximately 90 km in the South China Sea. This study investigates the IRA's ability to observe ocean sea surface height (SSH) across scales ranging from meters to mesoscale. The sea surface height anomaly (SSHA) of the IRA is aligned with the SSHA of the AVISO at scales greater than 30 km, but also demonstrates the ability to capture small-scale SSHA changes in two dimensions. We analyzed wavenumber spectra of SSHA obtained from the airborne IRA, ICESat-2, and SARAL/AltiKa satellite for this region. The results show a good agreement in power spectral density (PSD) levels between ICESat-2, SARAL/AltiKa and IRA at scales larger than 30 km. Within the submesoscale range of 1–10 km, the IRA SSHA spectrum exhibits a distinctly negative slope and the lowest energy level. The minimum PSD level of the IRA fell in the range of 10−4–10−3 m2/cycle/km, at scales around 1 km, which is more than an order of magnitude lower than that of ICESat-2, forming a spectral gap that is in agreement with the theoretical expectation. Furthermore, IRA-derived wave direction and significant wave height matched well with the MFWAM wave data. The results of this study underscore the considerable potential of airborne IRA in capturing SSHA across a range of scales, from oceanic waves to submesoscale. [ABSTRACT FROM AUTHOR]

The large quantity of resident space objects orbiting Earth poses a threat to safety and efficient operations in space. Radar sensors are well suited to detecting objects in space including decommissioned satellites and debris, whereas the more commonly used optical sensors are limited by daylight and weather conditions. Observations of three non-operational satellites using a VHF radar system are presented in this paper in the form of micro Doppler signatures associated with rotational motion. Micro Doppler signatures are particularly useful for characterising resident space objects at VHF given the limited bandwidth resulting in poor range resolution. Electromagnetic simulations of the micro Doppler signatures of the defunct satellites are also presented using simple computer-aided design (CAD) models to assist with interpretation of the radar observations. The simulated micro Doppler results are verified using the VHF radar data and provide insight into the attitude and spin axis of the three resident space objects. As future work, this approach will be extended to a larger number of resident space objects which requires a automated processing. [ABSTRACT FROM AUTHOR]

The Yulong porphyry copper belt in eastern Tibet is located in the middle of Tethys–Himalayan metallogenic mega-province, which is one of the three major porphyry copper metallogenic mega-provinces. The Yulong copper belt belongs to the super porphyry copper belt and represents one of the most important copper mineralization prospecting areas in China. A significant quantity of research data shows that this study area belongs to the environment of intracontinental collision and compression, with a complex geological structure, magmatic rock development and excellent metallogenic geological background. However, because this area is located in an alpine and high-altitude area, it is difficult to carry out any traditional field geological surveys, and the existing studies of both prospecting and prediction are relatively weak. This study focused on information extraction for alteration minerals in the Yulong metallogenic belt and its surroundings based on multispectral data and hyperspectral data, establishing a spectral library of alteration minerals in this area. Based on Sentinel-1A radar data and Landsat-8 OLI color synthesis data, the linear structure of the study area was interpreted. On this basis, the information extraction results relating to alteration minerals obtained from multi-source remote sensing data, linear structure interpretation results and the geochemical exploration data of the study area were superimposed to comprehensively analyze the metallogenic geological conditions and mineralization characteristics in the area, establish remote sensing prospecting indicators there and optimize the potential areas for prospecting, providing technical support for the next step of prospecting and exploration in the area. [ABSTRACT FROM AUTHOR]

This paper details the design and implementation of a harmonic frequency-modulated continuous-wave (FMCW) radar system, specialized in detecting harmonic tags and achieving precise range estimation. Operating within the 2.4–2.5 GHz frequency range for the forward channel and 4.8–5.0 GHz for the backward channel, this study delves into the various challenges faced during the system's realization. These challenges include selecting appropriate components, calibrating the system, processing signals, and integrating the system components. In addition, we introduce a single-layer passive harmonic tag, developed specifically for assessing the system, and provide an in-depth theoretical analysis and simulation results. Notably, the system is characterized by its low power consumption, making it particularly suitable for short-range applications. The system's efficacy is further validated through experimental evaluations in a real-world indoor environment across multiple tag positions. Our measurements underscore the system's robust ranging accuracy and its ability to mitigate self-interference, showcasing its significant potential for applications in harmonic tag detection and ranging. [ABSTRACT FROM AUTHOR]

Activity recognition is one of the significant technologies accompanying the development of the Internet of Things (IoT). It can help in recording daily life activities or reporting emergencies, thus improving the user's quality of life and safety, and even easing the workload of caregivers. This study proposes a human activity recognition (HAR) system based on activity data obtained via the micro-Doppler effect, combining a two-stream one-dimensional convolutional neural network (1D-CNN) with a bidirectional gated recurrent unit (BiGRU). Initially, radar sensor data are used to generate information related to time and frequency responses using short-time Fourier transform (STFT). Subsequently, the magnitudes and phase values are calculated and fed into the 1D-CNN and Bi-GRU models to extract spatial and temporal features for subsequent model training and activity recognition. Additionally, we propose a simple cross-channel operation (CCO) to facilitate the exchange of magnitude and phase features between parallel convolutional layers. An open dataset collected through radar, named Rad-HAR, is employed for model training and performance evaluation. Experimental results demonstrate that the proposed 1D-CNN+CCO-BiGRU model demonstrated superior performance, achieving an impressive accuracy rate of 98.2%. This outperformance of existing systems with the radar sensor underscores the proposed model's potential applicability in real-world scenarios, marking a significant advancement in the field of HAR within the IoT framework. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach for wide angle monitoring of vital signs in smart home applications. The person is tracked using an indoor radar. Upon detecting the person to be static, the radar automatically focuses its beam on that location, and subsequently breathing and heart rates are extracted from the reflected signals using continuous wavelet transform ( C W T ) analysis. In this way, leveraging the radar's on-chip processor enables real-time monitoring of vital signs across varying angles. In our experiment, we employ a commercial multi-input multi-output (MIMO) millimeter-wave FMCW radar to monitor vital signs within a range of 1.15 to 2.3 m and an angular span of − 44.8 to + 44.8 deg. In the Bland–Altman plot, the measured results indicate the average difference of − 1.5 and 0.06 beats per minute (BPM) relative to the reference for heart rate and breathing rate, respectively. [ABSTRACT FROM AUTHOR]

In this study, we investigate the seasonal and diurnal variations in cloud occurrence frequency, as well as cloud vertical structure (CVS) characteristics under different seasons and precipitation intensities over the Guangzhou region in South China, based on the analysis of millimeter-wave cloud radar (MMCR) and ground automatic weather station rainfall observations from May 2019 to August 2021. The results showed that the occurrence frequency of clouds exhibits a bimodal distribution throughout the year, with peaks in March to June and October, reaching its highest occurrence in May at approximately 80% and its lowest from December to February at around 40%. Additionally, there are distinct diurnal variations in occurrence frequency, with the lowest rates occurring around 0005 LST, rapidly increasing after 0006 LST, and peaking during the afternoon to evening hours. Cloud top height (CTH) shows bimodal distributions during the pre-flood and post-flood seasons. The most frequently occurring range of CTH during the pre-flood season is below 3 km, accounting for approximately 43%, while during the post-flood season, it ranges from 11 to 14 km, constituting about 37%. For precipitation clouds, CTH can extend beyond 12 km, with the radar reflectivity decreasing gradually with increasing height. The highest frequencies of radar echoes are observed below 2 km and between 4 and 7 km, exhibiting clear diurnal variations, with echoes mainly below 2 km and between 4 to 6 km during the early morning, intensifying and shifting to higher altitudes during the day and reaching their maximum below 4 km during the afternoon to nighttime hours, while both the frequency and intensity increase in the height range of 4 to 12 km. Vertical profiles of radar reflectivity and cloud ice/liquid water content (IWC/LWC) exhibit similar trends under different precipitation intensities. The main differences are observed below 4 km, where both radar reflectivity and IWC/LWC generally increase with increasing precipitation intensity. These findings contribute to a better understanding of cloud characteristics in the South China region, enhance the accuracy of model simulations, and provide a scientific basis for accurate forecasting and warning of meteorological disasters. [ABSTRACT FROM AUTHOR]

In this paper, we propose an innovative approach for transforming 2D human pose estimation into 3D models using Single Input–Single Output (SISO) Ultra-Wideband (UWB) radar technology. This method addresses the significant challenge of reconstructing 3D human poses from 1D radar signals, a task traditionally hindered by low spatial resolution and complex inverse problems. The difficulty is further exacerbated by the ambiguity in 3D pose reconstruction, as multiple 3D poses may correspond to similar 2D projections. Our solution, termed the Radar PoseLifter network, leverages the micro-Doppler signatures inherent in 1D radar echoes to effectively convert 2D pose information into 3D structures. The network is specifically designed to handle the long-range dependencies present in sequences of 2D poses. It employs a fully convolutional architecture, enhanced with a dilated temporal convolutions network, for efficient data processing. We rigorously evaluated the Radar PoseLifter network using the HPSUR dataset, which includes a diverse range of human movements. This dataset comprises data from five individuals with varying physical characteristics, performing a variety of actions. Our experimental results demonstrate the method's robustness and accuracy in estimating complex human poses, highlighting its effectiveness. This research contributes significantly to the advancement of human motion capture using radar technology. It presents a viable solution for applications where precision and reliability in motion capture are paramount. The study not only enhances the understanding of 3D pose estimation from radar data but also opens new avenues for practical applications in various fields. [ABSTRACT FROM AUTHOR]

There is increasing interest in space domain awareness worldwide, motivating investigation of the use of non-traditional sensors for space surveillance. One such class of sensor is VHF wind profiling radars, which have a low cost relative to other radars typically applied to this task. These radars are ubiquitous throughout the world and may potentially offer complementary space surveillance capabilities to the Space Surveillance Network. This paper updates an initial investigation on the use of Buckland Park VHF wind profiling radars for observing resident space objects in low Earth orbit to further investigate the space surveillance capabilities of the sensor class. The radar was operated during the Australian Defence "SpaceFest" 2019 activity, incorporating new beam scheduling and signal processing functionality that extend upon the capabilities described in the initial investigation. The beam scheduling capability used two-line element propagations to determine the appropriate beam direction to use to observe transiting satellites. The signal processing capabilities used a technique based on the Keystone transform to correct for range migration, allowing the development of new signal processing modes that allow the coherent integration time to be increased to improve the SNR of the observed targets, thereby increasing the detection rate. The results reveal that 5874 objects were detected over 10 days, with 2202 unique objects detected, representing a three-fold increase in detection rate over previous single-beam direction observations. The maximum detection height was 2975.4 km, indicating a capability to detect objects in medium Earth orbit. A minimum detectable RCS at 1000 km of −10.97 dBm2 (0.09 m2) was observed. The effects of Faraday rotation resulting from the use of linearly polarised antennae are demonstrated. The radar's utility for providing total electron content (TEC) measurements is investigated using a high-range resolution mode and high-precision ephemeris data. The short-term Fourier transform is applied to demonstrate the radar's ability to investigate satellite rotation characteristics and monitor ionospheric plasma waves and instabilities. [ABSTRACT FROM AUTHOR]

The polar regions of Mars, including the South and North Poles, are crucial for studying Martian climate and geological history, as they contain the largest reservoir of subsurface water ice. This study introduces a new approach for reflector detection, which includes radargram denoising to effectively enhance the signal of underground reflectors, peak detection to extract the positions of subsurface stratification from the radar echoes, and peak points connection to form continuous layers. The mapped enhancement denoising process involves a linear brightness adjustment and a fourth-order diffusion equation to enhance the signal of the subsurface layers for effective detection. The subsurface detection extracts the surface and subsurface peak points based on a peak detection algorithm, while using locally window-enhanced peak filtering and Kullback–Leibler (KL) divergence mapping to filter out non-stratified peak points. Finally, the layered connection process uses the proximity parameter to connect peak points in the same layer. Applied to multiple SHARAD (Shallow Radar) images at the Martian poles, this algorithm demonstrated a false detection rate below 5%. Compared to other methods, this method has a missed detection rate of less than 5% and, additionally, exhibits fewer discontinuities in layer connectivity. Therefore, this algorithm shows exceptional proficiency and applicability in analyzing the complex subsurface structures of the Martian polar regions. [ABSTRACT FROM AUTHOR]

Radar emitter signal sorting (RESS) is a crucial process in contemporary electronic battlefield situation awareness. Separating pulses belonging to the same radar emitter from interleaved radar pulse sequences with a lack of prior information, high density, strong overlap, and wide parameter distribution has attracted increasing attention. In order to improve the accuracy of RESS under scenarios with limited labeled samples, this paper proposes an RESS model called TR-RAGCN-AFF-RESS. This model transforms the RESS problem into a pulse-by-pulse classification task. Firstly, a novel weighted adjacency matrix construction method was proposed to characterize the structural relationships between pulse attribute parameters more accurately. Building upon this foundation, two networks were developed: a Transformer(TR)-based interleaved pulse sequence temporal feature extraction network and a residual attention graph convolutional network (RAGCN) for extracting the structural relationship features of attribute parameters. Finally, the attention feature fusion (AFF) algorithm was introduced to fully integrate the temporal features and attribute parameter structure relationship features, enhancing the richness of feature representation for the original pulses and achieving more accurate sorting results. Compared to existing deep learning-based RESS algorithms, this method does not require many labeled samples for training, making it better suited for scenarios with limited labeled sample availability. Experimental results and analysis confirm that even with only 10% of the training samples, this method achieves a sorting accuracy exceeding 93.91%, demonstrating high robustness against measurement errors, lost pulses, and spurious pulses in non-ideal conditions. [ABSTRACT FROM AUTHOR]

NASA's Soil Moisture Active Passive (SMAP) was originally designed to combine high-resolution active (radar) and coarse-resolution but highly sensitive passive (radiometer) L-band observations to achieve unprecedented spatial resolution and accuracy for soil moisture retrievals. However, shortly after SMAP was put into orbit, the radar component failed, and the high-resolution capability was lost. In this paper, the integration of an alternative radar sensor with the SMAP radiometer is proposed to enhance soil moisture retrieval capabilities over vegetated areas in the absence of the original high-resolution radar in the SMAP mission. ESA's Sentinel-1A C-band radar was used in this study to enhance the spatial resolution of the SMAP L-band radiometer and to improve soil moisture retrieval accuracy. To achieve this purpose, we downscaled the 9 km radiometer data of the SMAP to 1 km utilizing the Smoothing Filter-based Intensity Modulation (SFIM) method. An Artificial Neural Network (ANN) was then trained to exploit the synergy between the Sentinel-1A radar, SMAP radiometer, and the in situ-measured soil moisture. An analysis of the data obtained for a plant growing season over the Mississippi Delta showed that the VH-polarized Sentinel-1A radar data can yield a coefficient of correlation of 0.81 and serve as a complimentary source to the SMAP radiometer for more accurate and enhanced soil moisture prediction over agricultural fields. [ABSTRACT FROM AUTHOR]

Through-wall radar human body pose recognition technology has broad applications in both military and civilian sectors. Identifying the current pose of targets behind walls and predicting subsequent pose changes are significant challenges. Conventional methods typically utilize radar information along with machine learning algorithms such as SVM and random forests to aid in recognition. However, these approaches have limitations, particularly in complex scenarios. In response to this challenge, this paper proposes a cross-modal supervised through-wall radar human body pose recognition method. By integrating information from both cameras and radar, a cross-modal dataset was constructed, and a corresponding deep learning network architecture was designed. During training, the network effectively learned the pose features of targets obscured by walls, enabling accurate pose recognition (e.g., standing, crouching) in scenarios with unknown wall obstructions. The experimental results demonstrated the superiority of the proposed method over traditional approaches, offering an effective and innovative solution for practical through-wall radar applications. The contribution of this study lies in the integration of deep learning with cross-modal supervision, providing new perspectives for enhancing the robustness and accuracy of target pose recognition. [ABSTRACT FROM AUTHOR]

In the realm of road safety and the evolution toward automated driving, Advanced Driver Assistance and Automated Driving (ADAS/AD) systems play a pivotal role. As the complexity of these systems grows, comprehensive testing becomes imperative, with virtual test environments becoming crucial, especially for handling diverse and challenging scenarios. Radar sensors are integral to ADAS/AD units and are known for their robust performance even in adverse conditions. However, accurately modeling the radar's perception, particularly the radar cross-section (RCS), proves challenging. This paper adopts a data-driven approach, using Gaussian mixture models (GMMs) to model the radar's perception for various vehicles and aspect angles. A Bayesian variational approach automatically infers model complexity. The model is expanded into a comprehensive radar sensor model based on object lists, incorporating occlusion effects and RCS-based detectability decisions. The model's effectiveness is demonstrated through accurate reproduction of the RCS behavior and scatter point distribution. The full capabilities of the sensor model are demonstrated in different scenarios. The flexible and modular framework has proven apt for modeling specific aspects and allows for an easy model extension. Simultaneously, alongside model extension, more extensive validation is proposed to refine accuracy and broaden the model's applicability. [ABSTRACT FROM AUTHOR]

Amplification of wideband high-frequency and microwave signals is a fundamental element within every high-frequency circuit and device. Ultra-wideband (UWB) sensor applications use circuits designed for their specific application. The article presents the analysis, design, and implementation of ultra-wideband differential amplifiers for M-sequence-based UWB applications. The designed differential amplifiers are based on the Cherry–Hooper structure and are implemented in a low-cost 0.35 µm SiGe BiCMOS semiconductor process. The article presents an analysis and realization of several designs focused on different modifications of the Cherry–Hooper amplifier structure. The proposed amplifier modifications are focused on achieving the best result in one main parameter's performance. Amplifier designs modified by capacitive peaking to achieve the largest bandwidth, amplifiers with the lowest possible noise figure, and designs focused on achieving the highest common mode rejection ratio (CMRR) are described. The layout of the differential amplifiers was created and the chip was manufactured and wire-bonded to the QFN package. For evaluation purposes, a high-frequency PCB board was designed. Schematic simulations, post-layout simulations, and measurements of the individual parameters of the designed amplifiers were performed. The designed and fabricated ultra-wideband differential amplifiers have the following parameters: a supply current of 100–160 mA at −3.3 V or 3.3 V, bandwidth from 6 to 12 GHz, gain (at 1 GHz) from 12 to 16 dB, noise figure from 7 to 13 dB, and a common mode rejection ratio of up to 70 dB. [ABSTRACT FROM AUTHOR]

The potential of microwave Doppler radar in non-contact vital sign detection is significant; however, prevailing radar-based heart rate (HR) and heart rate variability (HRV) monitoring technologies often necessitate data lengths surpassing 10 s, leading to increased detection latency and inaccurate HRV estimates. To address this problem, this paper introduces a novel network integrating a frequency representation module and a residual in residual module for the precise estimation and tracking of HR from concise time series, followed by HRV monitoring. The network adeptly transforms radar signals from the time domain to the frequency domain, yielding high-resolution spectrum representation within specified frequency intervals. This significantly reduces latency and improves HRV estimation accuracy by using data that are only 4 s in length. This study uses simulation data, Frequency-Modulated Continuous-Wave radar-measured data, and Continuous-Wave radar data to validate the model. Experimental results show that despite the shortened data length, the average heart rate measurement accuracy of the algorithm remains above 95% with no loss of estimation accuracy. This study contributes an efficient heart rate variability estimation algorithm to the domain of non-contact vital sign detection, offering significant practical application value. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) is a rapid and non-destructive geophysical technique widely employed to detect and quantify subsurface structures and characteristics. Its capability for time lapse (TL) detection provides essential insights into subsurface hydrological dynamics, including lateral flow and soil water distribution. However, during TL-GPR surveys, field conditions often create discrepancies in surface geometry, which introduces mismatches across sequential TL-GPR images. These discrepancies may generate spurious signal variations that impede the accurate interpretation of TL-GPR data when assessing subsurface hydrological processes. In responding to this issue, this study introduces a TL-GPR image alignment method by employing the dynamic time warping (DTW) algorithm. The purpose of the proposed method, namely TLIAM–DTW, is to correct for geometric mismatch in TL-GPR images collected from the identical survey line in the field. We validated the efficacy of the TLIAM–DTW method using both synthetic data from gprMax V3.0 simulations and actual field data collected from a hilly, forested area post-infiltration experiment. Analyses of the aligned TL-GPR images revealed that the TLIAM–DTW method effectively eliminates the influence of geometric mismatch while preserving the integrity of signal variations due to actual subsurface hydrological processes. Quantitative assessments of the proposed methods, measured by mean absolute error (MAE) and root mean square error (RMSE), showed significant improvements. After performing the TLIAM–DTW method, the MAE and RMSE between processed TL-GPR images and background images were reduced by 96% and 78%, respectively, in simple simulation scenarios; in more complex simulations, MAE declined by 27–31% and RMSE by 17–43%. Field data yielded reductions in MAE and RMSE of >82% and 69%, respectively. With these substantial improvements, the processed TL-GPR images successfully depict the spatial and temporal transitions associated with subsurface lateral flows, thereby enhancing the accuracy of monitoring subsurface hydrological processes under field conditions. [ABSTRACT FROM AUTHOR]

Ice thickness has a significant effect on the physical and biogeochemical processes of a lake, and it is an integral focus of research in the field of ice engineering. The Qinghai–Tibetan Plateau, known as the Third Pole of the world, contains numerous lakes. Compared with some information, such as the area, water level, and ice phenology of its lakes, the ice thickness of these lakes remains poorly understood. In this study, we used an unmanned aerial vehicle (UAV) with a 400/900 MHz ice-penetrating radar to detect the ice thickness of Qinghai Lake and Gahai Lake. Two observation fields were established on the western side of Qinghai Lake and Gahai Lake in January 2019 and January 2021, respectively. Based on the in situ ice thickness and the propagation time of the radar, the accuracy of the ice thickness measurements of these two non-freshwater lakes was comprehensively assessed. The results indicate that pre-processed echo images from the UAV-borne ice-penetrating radar identified non-freshwater lake ice, and we were thus able to accurately calculate the propagation time of radar waves through the ice. The average dielectric constants of Qinghai Lake and Gahai Lake were 4.3 and 4.6, respectively. This means that the speed of the radar waves that propagated through the ice of the non-freshwater lake was lower than that of the radio waves that propagated through the freshwater lake. The antenna frequency of the radar also had an impact on the accuracy of ice thickness modeling. The RMSEs were 0.034 m using the 400 MHz radar and 0.010 m using the 900 MHz radar. The radar with a higher antenna frequency was shown to provide greater accuracy in ice thickness monitoring, but the control of the UAV's altitude and speed should be addressed. [ABSTRACT FROM AUTHOR]

To evaluate the impact of different momentum control variable (CV) schemes (CV5, the momentum control variable option with ψχ and CV7, the momentum control variable option with UV) on radar data assimilation (DA) in weather research and forecasting model data-assimilation (WRFDA) systems, a heavy snowfall in central and eastern regions of China, which started on 6 February 2022, was taken as a case in this study. The results of the wind-field increments from the single observation tests indicated that the wind-field increments had a larger range of influence when stream function and velocity potential (ψχ) were used as momentum control variables in CV5. Some spurious increments were also generated in the wind-field analysis, since CV5 tended to maintain the integrated value of the wind field. When U-wind and V-wind were used as control variables in CV7, the wind-field increments had a smaller impact range, and there was less dependence among different locations on the wind increments. For the heavy snow case, the CV7 schemes displayed some improvements in simulating the composite reflectivity compared to the other two experiments, since the composite reflectivity in the CV5 and control experiments were overestimated to some level. It was also found that the RMSEs were lower in the CV7 compared to those in the CV5 in the short-term forecasts during the data-assimilation cycles. Results also indicated that the CV7 had a more significant effect on the 6 h accumulated precipitation forecasts. Meanwhile, the experiment Exp_CV7 achieved the best ETS and FSS scores among the three groups of experiments, while Exp_CV5 appeared to be generally superior to the CTRL. In summary, the precipitation of Exp_CV7 yielded the rainfall intensity and location most close to the observation compared to those from both the CTRL and Exp_CV5 experiments. [ABSTRACT FROM AUTHOR]

Based on ground observed data, S-band dual-polarization radar data, and ERA-5 reanalysis data, the statistical characteristics of polarimetric parameters and the application of melting layer (ML) and hydrometeor classification (HCL) products during eight snowstorm events in Jiangsu Province from 2020 to 2022 were investigated. A heavy snowstorm that went through different phases of rain, sleet, and pure snow and that occurred on 29 December 2020 was also analyzed as a typical example. The results showed the following: During the phase transition between rain and snow in the Jiangsu region, the basic reflectivity factor ZH ≥ 27 dBZ, the zero-order lag correlation coefficient CC ≤ 0.93, and the differential reflectivity ZDR ≥ 1.0 dB were important indicators for judging the melting layer while the specific differential phase KDP changed slightly. The snowstorm event was well observed and recorded by the Yancheng dual-polarimetric radar, whose low value area of CC coincided mostly with the melting layer. The ML products and HCL products based on fuzzy-logic hydrometeor classification algorithms can help identify the melting layer and the properties of precipitation particles. ML products are more reliable when the melting layer is high and can better show the trends of melting layer decline. They can certainly serve as a reference for detecting and judging precipitation phase changes in winter in Jiangsu Province. [ABSTRACT FROM AUTHOR]

Some studies have shown that any part machined by an outsourcer usually has several basic quality characteristics. When the outsourcer's process capabilities are insufficient, the defective rate of various quality characteristics of the product will increase, thereby raising the rework rate and scrap rate. As a result, maintenance costs will go up, economic value will decrease, and even carbon emissions can increase during the production process. In addition, the process capability index and the radar chart are widely used in engineering management and other fields. Since process indicators often contain unknown parameters, sample data are needed for evaluation. With the rapid development of the Internet of Things and big data analysis, many companies regard rapid response as a basic requirement for timeliness and cost consideration. Therefore, companies often have to evaluate the process quality of ten small samples and decide whether to make some improvements. In order to solve the above problems, this study proposed a fuzzy radar chart evaluation model for the process quality of multi-quality characteristic parts based on the process capability index. Using this model can help all parts manufacturers continue to improve the quality of their machined parts as well as reduce their rework and scrap rates. Meanwhile, carbon emissions can be lessened during the production process, and companies can fulfill their social responsibilities. This fuzzy radar chart evaluation model is based on confidence intervals. As the company's past experience is incorporated, the evaluation accuracy can be maintained even with a smaller sample size. Furthermore, the fuzzy radar evaluation chart can simultaneously evaluate the process capabilities of all quality characteristics of the part. In addition to making it easier for manufacturers to master all quality characteristics, quality process capability can also help them seize improvement opportunities. [ABSTRACT FROM AUTHOR]

A simple microwave photonic, reconfigurable, instantaneous frequency measurement system based on low-voltage thin-film lithium niobate on an insulator phase modulator is put forward and experimentally demonstrated. Changing the wavelength of the optical carrier can realize the flexibility of the frequency measurement range and accuracy, showing that during the ranges of 0–10 GHz, 3–15 GHz, and 12–18 GHz, the average measurement errors are 26.9 MHz, 44.57 MHz, and 13.6 MHz, respectively, thanks to the stacked integrated learning models. Moreover, this system is still able to respond to microwave signals of as low as −30 dBm with the frequency measurement error of 62.06 MHz, as that low half-wave voltage for the phase modulator effectively improves the sensitivity of the system. The general-purpose, miniaturized, reconfigurable, instantaneous frequency measurement modules have unlimited potential in areas such as radar detection and early warning reception. [ABSTRACT FROM AUTHOR]

A fully integrated 24-GHz radar transceiver with one transmitter (TX) and two receivers (RXs) for compact frequency modulated continuous wave (FMCW) radar applications is here presented. The FMCW synthesizer was realized using a fractional-N phase-locked loop (PLL) and programmable chirp generator, which are completely integrated in the proposed transceiver. The measured output phase noise of the synthesizer is −80 dBc/Hz at 100 kHz offset. The TX consists of a three-bit bridged t-type attenuator for gain control, a two-stage drive amplifier (DA) and a one-stage power amplifier (PA). The TX chain provides an output power of 13 dBm while achieving <0.5 dB output power variation within the range of 24 to 24.25 GHz. The RX with a direct conversion I-Q structure is composed of a two-stage low noise amplifier (LNA), I-Q generator, mixer, transimpedance amplifier (TIA), a two-stage biquad band pass filter (BPF), and a differential-to-single (DTS) amplifier. The TIA and the BPF employ a DC offset cancellation (DCOC) circuit to suppress the strong reflection signal and TX-RX leakage. The RX chain exhibits an overall gain of 100 dB. The proposed radar transceiver is fabricated using a 65 nm CMOS technology. The transceiver consumes 220 mW from a 1 V supply voltage and has 4.84 mm2 die size including all pads. The prototype FMCW radar is realized with the proposed transceiver and Yagi antenna to verify the radar functionality, such as the distance and angle of targets. [ABSTRACT FROM AUTHOR]

Numerous analytical radar-scattering laws have been published through the past decades to interpret planetary radar observations, such as Hagfors' law, which has been commonly used for the Moon, and the cosine law, which is commonly used in the shape modeling of asteroids. Many of the laws have not been numerically validated in terms of their interpretation and limitations. This paper evaluates radar-scattering laws for self-affine fractal surfaces using a numerical approach. Traditionally, the autocorrelation function and, more recently, the Hurst exponent, which describes the self-affinity, have been used to quantify the height correlation. Here, hundreds of three-dimensional synthetic surfaces parameterized using a root-mean-square (rms) height and a Hurst exponent were generated, and their backscattering coefficient functions were computed to evaluate their consistency with selected analytical models. The numerical results were also compared to empirical models for roughness and radar-scattering measurements of Hawaii lava flows and found consistent. The Gaussian law performed best at predicting the rms slope regardless of the Hurst exponent. Consistent with the literature, it was found to be the most reliable radar-scattering law for the inverse modeling of the rms slopes and the Fresnel reflection coefficient from the quasi-specular backscattering peak, when homogeneous statistical properties and a ray-optics approach can be assumed. The contribution of multiple scattering in the backscattered power increases as a function of rms slope up to about 20% of the backscattered power at normal incidence when the rms slope angle is 46°. [ABSTRACT FROM AUTHOR]

Detecting multiple human targets in indoor scenarios using ultra-wideband (UWB) radar usually involves false detection results caused by the secondary reflections, which might reduce the target detection accuracy and cause a more severe deterioration when the number of targets increases. This article proposed a two-step accuracy improvement method for multitarget detection in environments with multiple human targets of more than three and strong secondary reflections by the surroundings, especially the walls. Based on the rough detection results acquired by the modified CA-CFAR (MCA-CFAR) processing, the first step achieves the primary false alarm suppression using a short-window accumulation in the time domain. Then, the second step applies the decision confidence on the detection results from the first step to assess the reliability of results for improved accuracy. The two-step accuracy improvement could thus have a higher accuracy through cascading false alarm suppression. The effectiveness and accuracy of the proposed algorithm are verified based on the experimental results. [ABSTRACT FROM AUTHOR]

The Biomass Proxy is a new cloud-free vegetation monitoring product that offers timely and analysis-ready data indicative of above-ground crop biomass dynamics at 10m spatial resolution. The Biomass Proxy links the consistent and continuous temporal signal of the Sentinel-1 Cross Ratio (CR), a vegetation index derived from Synthetic Aperture Radar backscatter, with the spatial information of the Sentinel-2 Normalized Difference Vegetation Index (NDVI), a vegetation index derived from optical observations. A global scaling relationship between CR and NDVI forms the basis of a novel fusion methodology based on static and dynamic combinations of temporal and spatial responses of CR and NDVI at field level. The fusion process is used to mitigate the impact on product quality of low satellite revisit periods due to acquisition design or persistent cloud coverage, and to respond to rapid changes in a timely manner to detect environmental and management events. The resulting Biomass Proxy provides time series that are continuous, unhindered by clouds, and produced uniformly across all geographical regions and crops. The Biomass Proxy offers opportunities including improved crop growth monitoring, event detection, and phenology stage detection. [ABSTRACT FROM AUTHOR]

The planet Mars is the most probable among the terrestrial planets in our solar system to support human settlement or colonization in the future. The detection of water ice or liquid water on the shallow subsurface of Mars is a crucial scientific objective for both the Chinese Tianwen-1 and United States Mars 2020 missions, which were launched in 2020. Both missions were equipped with Rover-mounted ground-penetrating radar (GPR) instruments, specifically the RoPeR on the Zhurong rover and the RIMFAX radar on the Perseverance rover. The in situ radar provides unprecedented opportunities to study the distribution of shallow subsurface water ice on Mars with its unique penetrating capability. The presence of water ice on the shallow surface layers of Mars is one of the most significant indicators of habitability on the extraterrestrial planet. A considerable amount of evidence pointing to the existence of water ice on Mars has been gathered by previous researchers through remote sensing photography, radar, measurements by gamma ray spectroscopy and neutron spectrometers, soil analysis, etc. This paper aims to review the various approaches utilized in detecting shallow subsurface water ice on Mars to date and to sort out the past and current evidence for its presence. This paper also provides a comprehensive overview of the possible clues of shallow subsurface water ice in the landing area of the Perseverance rover, serving as a reference for the RIMFAX radar to detect water ice on Mars in the future. Finally, this paper proposes the future emphasis and direction of rover-mounted radar for water ice exploration on the Martian shallow subsurface. [ABSTRACT FROM AUTHOR]

Metamaterial-based coded aperture imaging (MCAI) is a forward-looking radar imaging technique based on wavefront modulation. The scattering coefficients of the target can resolve as an ill-posed inverse problem. Data-based deep-learning methods provide an efficient, but expensive, way for target reconstruction. To address the difficulty in collecting paired training data, an untrained deep radar-echo-prior-based MCAI (DMCAI) optimization model is proposed. DMCAI combines the MCAI model with a modified U-Net for predicting radar echo. A joint loss function based on deep-radar echo prior and total variation is utilized to optimize network weights through back-propagation. A target reconstruction strategy by alternatively using the imaginary and real part of the radar echo signal (STAIR) is proposed to solve the DMCAI. It makes the target reconstruction task turn into an estimation from an input image by the U-Net. Then, the optimized weights serve as a parametrization that bridges the input image and the target. The simulation and experimental results demonstrate the effectiveness of the proposed approach under different SNRs or compression measurements. [ABSTRACT FROM AUTHOR]

Employing different bands of radar to detect precipitation information in identical regions enables the acquisition of a more comprehensive precipitation cloud structure, thereby refining the continuity and completeness of precipitation measurements. This study first compared the coincident data from CloudSat W-band cloud profiling radar (CPR) and Global Precipitation Measurement Mission (GPM) Ka-band precipitation radar (KaPR) from 2014 to 2017, and then a synthesis of the radar reflectivity from CPR and KaPR was attempted to obtain a complete cloud and precipitation structure. The findings of the reflectivity comparisons indicated that the echo-top height identified by CPR is on average 3.6 to 4.2 km higher than that from KaPR, due to the higher sensitivity. Because of strong attenuation of CPR by liquid-phase particles, the reflectivity below the height of the melting layer usually shows an opposite gradient to KaPR with decreasing altitude. The difference in the near-surface rain rates of CPR and KaPR was found to be related to reflectivity gradients in the vertical direction, and the larger the reflectivity gradients, the greater the differences in near-surface rain rates. For better representing the complete vertical structure of precipitation clouds and improving the consistency of the reflectivity and precipitation rate, the radar reflectivity was weighted, synthesized from CPR and KaPR based on the gradient difference of the reflectivity from the two radars. We presented the synthesis results for a stratiform cloud and a deep convective case, and Spearman's rank correlation coefficient (rs) between the GPM combined radiometer precipitation rate and the radar reflectivity was utilized to analyze the performance of the synthesis. The consistency between synthesized reflectivity and precipitation rate in the non-liquid phase was improved compared with KaPR, and the rs of the ice and mixed phases was increased by about 12% and 10%, respectively. [ABSTRACT FROM AUTHOR]

The increasing amount of space debris poses a significant threat to operational satellites and space-based services. This article updates the community on the current status of the development of ATLAS, a tracking radar that is part of the EUSST network and aims to detect space objects in low Earth orbits. This article focuses on the latest activities performed: calibration of the pointing system and initial observations of space objects. The calibration procedure consisted of cross-scanning the Solar disk and yielded great results, obtaining an offset of 5.3° in azimuth and 0.10° in elevation. The first observation campaign resulted in 33 range detections of the International Space Station (ISS) with a probability of false alarm of  10 − 9 . The observations were then used to readjust the radar equation to assess the real-world performance of the system. [ABSTRACT FROM AUTHOR]

This paper presents a method for predicting atmospheric duct conditions from a clutter power spectrum using deep learning. To accurately predict the duct conditions, deep learning with a binary classification is applied to the proposed refractivity from the clutter (RFC) method. The input data set is the artificial clutter data that are generated via the Advanced Refractive Prediction System (AREPS) simulation software Ver. 3.6 in conjunction with random atmospheric refractive indices. The output of the RFC method is then predicted via binary classification, indicating whether the atmospheric conditions are duct or non-duct. For the cross-validation, the clutter power spectrum data are generated based on real atmospheric refractivity data. The results show that the DNN trained with 5600 pieces of data (validation accuracy of 95.99%) exhibits a binary classification accuracy of 98.36%. The deep neural network (DNN) trained with 28,000 pieces of data (validation accuracy of 98.20%) achieves a binary classification accuracy of 99.06% with an F1-score of 0.9921. [ABSTRACT FROM AUTHOR]

The processes of ocean dynamics are complex near the Pearl River Estuary and are not clear due to a lack of abundant observations. The spatial characteristics of the spring sea surface currents in the adjacent waters of the Pearl River Estuary were analyzed using the current data observed by a three-station high-frequency surface wave radar system (HFSWRS). Compared with the two-station HFSWRS, the deviation of current velocity and direction observed by the three-station HFSWRS from the underway measurements decreased by 42.86% and 38.30%, respectively. The analyzed results show that the M2 tidal current is the dominant current among all the tidal constituents, followed by K1, with angles of inclination ranging from 130° to 150°. The tidal flow is dominated by northwest–southeast back-and-forth flow. In the southern part of the observed area, which is far from the coastline, the tidal current ellipses exhibit a circular pattern. The prevalent tidal current type in this region is irregularly semi-diurnal, and the shallow water constituents also have a significant effect. The tidal energy in the adjacent waters of the Pearl River Estuary is affected by potential energy flux and kinetic energy flux. As the water depth and currents velocity increase in the southeast direction, the tidal energy flux increases. In the nearshore zone, the direction of tidal energy flux varies along the coastline. The changes in the residual current within the observed area are correlated with the sea surface wind field. Based on the high-precision sea surface current observed by the three-station HFSWRS, the characteristics of the ocean dynamic processes near the Pearl River Estuary are analyzed comprehensively, which provides important reference and confidence for the application of the developing new radar observing network with about 10 radar stations near the Pearl River Estuary. [ABSTRACT FROM AUTHOR]

Optimizing jamming strategies is crucial for enhancing the performance of cognitive jamming systems in dynamic electromagnetic environments. The emergence of frequency-agile radars, capable of changing the carrier frequency within or between pulses, poses significant challenges for the jammer to make intelligent decisions and adapt to the dynamic environment. This paper focuses on researching intelligent jamming decision-making algorithms for Intra-Pulse Frequency Agile Radar using deep reinforcement learning. Intra-Pulse Frequency Agile Radar achieves frequency agility at the sub-pulse level, creating a significant frequency agility space. This presents challenges for traditional jamming decision-making methods to rapidly learn its changing patterns through interactions. By employing Gated Recurrent Units (GRU) to capture long-term dependencies in sequence data, together with the attention mechanism, this paper proposes a GA-Dueling DQN (GRU-Attention-based Dueling Deep Q Network) method for jamming frequency selection. Simulation results indicate that the proposed method outperforms traditional Q-learning, DQN, and Dueling DQN methods in terms of jamming effectiveness. It exhibits the fastest convergence speed and reduced reliance on prior knowledge, highlighting its significant advantages in jamming the subpulse-level frequency-agile radar. [ABSTRACT FROM AUTHOR]

RADARs and cameras have been present in automotives since the advent of ADAS, as they possess complementary strengths and weaknesses but have been underlooked in the context of learning-based methods. In this work, we propose a method to perform object detection in autonomous driving based on a geometrical and sequential sensor fusion of 3+1D RADAR and semantics extracted from camera data through point cloud painting from the perspective view. To achieve this objective, we adapt PointPainting from the LiDAR and camera domains to the sensors mentioned above. We first apply YOLOv8-seg to obtain instance segmentation masks and project their results to the point cloud. As a refinement stage, we design a set of heuristic rules to minimize the propagation of errors from the segmentation to the detection stage. Our pipeline concludes by applying PointPillars as an object detection network to the painted RADAR point cloud. We validate our approach in the novel View of Delft dataset, which includes 3+1D RADAR data sequences in urban environments. Experimental results show that this fusion is also suitable for RADAR and cameras as we obtain a significant improvement over the RADAR-only baseline, increasing mAP from 41.18 to 52.67 (+27.9%). [ABSTRACT FROM AUTHOR]

Thunderstorms can generate intense electrical activity, hail, and result in substantial economic and human losses. The development of very short-term forecasting tools (nowcasting) is essential to provide information to alert systems in order to mobilize most efficiently the population. However, the development of nowcasting tools depends on a better understanding of the physics and microphysics of clouds and lightning formation and evolution. In this context, the objectives of this study are: (a) to describe the environmental conditions that led to a genesis of a thunderstorm that produce hail on 7 January 2012, in the Metropolitan Area of São Paulo (MASP) during the CHUVA-Vale campaign, and (b) to evaluate the thunderstorm microphysical properties and vertical structure of electrical charge. Data from different sources were used: field campaign data, such as S-band radar, and 2- and 3-dimensional lightning networks, satellite data from the Geostationary Operational Environmental Satellite-13 (GOES-13), the Meteosat Second Generation (MSG), and reanalysis of the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5). The thunderstorm developed in a region of low-pressure due to the presence of a near-surface inverted trough and moisture convergence, which favored convection. Convective Available Potential Energy (CAPE) of 1053.6 J kg−1 at the start of the thunderstorm indicated that strong convective energy was present. Microphysical variables such as Vertically Integrated Liquid water content (VIL) and Vertically Integrated Ice (VII) showed peaks of 140 and 130 kg m−2, respectively, before the hail reached the surface, followed by a decrease, indicating content removal from within the clouds to the ground surface. The thunderstorm charge structure evolved from a dipolar structure (with a negative center between 4 and 6 km and a positive center between 8 and 10 km) to a tripolar structure (negative center between 6 and 7.5 km) in the most intense phase. The first lightning peak (100 flashes in 5 min−1) before the hail showed that there had been a lightning jump. The maximum lightning occurred around 18:17 UTC, with approximately 350 flashes 5 min−1 with values higher than 4000 sources 500 m−1 in 5 min−1. Likewise, the vertical cross-sections indicated that the lightning occurred ahead of the thunderstorm's displacement (maximum reflectivity), which could be useful in predicting these events. [ABSTRACT FROM AUTHOR]

In this study, the nowcast models provided by the Python pySTEPS library were evaluated using radar derived rain rate data and the satellite product Split-Window Difference (SWD) based on GOES-16 data, focusing on central Mexico. Initially, we obtained a characterization of the rainfall that occurred in the region using the radar rain rate and the SWD. Subsequently the nowcasts were evaluated using both variables. Two nowcast models were employed from pySTEPS: Extrapolation and S-PROG. The results indicate that average SWD is below 2.5 K, 90 min before the onset of rainfall events, and, on average, the SWD is 2 K during rainfall events. The results from both nowcast models were accurate and produced similar results. The nowcasts performed better when SWD data were used as input, having an average Probability of Detection (PoD) above 70% and a False Alarm Rate (FAR) reaching 40% for the 15-min prediction. The nowcasts were less accurate using the radar rain rate as input for the 15-min forecast, where the PoD was maximum 70% and FAR reaching 40%. However, these nowcasts were more reliable during well-organized precipitation events. In this work, it was determined that the nowcast models provided by pySTEPS can provide valuable rain forecasts using GOES-16 satellite and radar data for the central Mexico region. [ABSTRACT FROM AUTHOR]

Surface currents obtained from the high-frequency surface-wave radars (HFSWRs) installed along the coast of Fujian Province are utilized to characterize submesoscale eddies in the southwestern Taiwan Strait from 29 January to 26 March 2013. The algorithm based on vector geometry (VG) has been applied to datasets and a total of 414 (161 anticyclonic and 253 cyclonic eddies) were obtained. Eddies with both rotations had a relatively short lifespan (≤3.7 h), and their radii were in the range of 5–22.5 km. Eddies with a lifespan of over 30 minutes were more likely to occur north of the Taiwan Strait shoals and move eastward or northeastward. The deviation of moving directions of eddies with a moving distance of more than 20 km was within 18°. Moreover, eddies could hardly hold their original forms with cyclones extending in the east-west and compressing in the north-south direction, and anticyclones were the opposite. The vorticity and strain rate were proportional to the square of the energy intensity (EI). This study shows that the array HFSWRs have a strong capability to observe short-lived submesoscale eddies. [ABSTRACT FROM AUTHOR]

Clouds are an important component of weather systems and are difficult to effectively characterize using current climate models and estimation of radiative forcing. Due to the limitations in observational capabilities, it remains difficult to obtain high-spatiotemporal-resolution, continuous, and accurate observations of clouds. To overcome this issue, we propose a novel and practical combined retrieval method using millimeter-wave radar and lidar, which enables the microphysical properties of thin liquid water clouds, such as cloud droplet effective radius, number concentration, and liquid water content, to be retrieved. This method was utilized to analyze the clouds observed at the Shanghai World Expo Park and was validated through synchronous observations with a microwave radiometer. Furthermore, the most suitable extinction backscatter ratio was determined through sensitivity analysis. This study provides vertical distributions of cloud microphysical properties with a time resolution of 1 min and a spatial resolution of 30 m, demonstrating the scientific potential of this combined retrieval method. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method for extracting the structure of an indoor environment using radar. When using the radar in an indoor environment, ghost targets are observed through the multipath propagation of radio waves. The presence of these ghost targets obstructs accurate mapping in the indoor environment, consequently hindering the extraction of the indoor environment. Therefore, we propose a deep learning-based method that uses image-to-image translation to extract the structure of the indoor environment by removing ghost targets from the indoor environment map. In this paper, the proposed method employs a conditional generative adversarial network (CGAN), which includes a U-Net-based generator and a patch-generative adversarial network-based discriminator. By repeating the process of determining whether the structure of the generated indoor environment is real or fake, CGAN ultimately returns a structure similar to the real environment. First, we generate a map of the indoor environment using radar, which includes ghost targets. Next, the structure of the indoor environment is extracted from the map using the proposed method. Then, we compare the proposed method, which is based on the structural similarity index and structural content, with the k-nearest neighbors algorithm, Hough transform, and density-based spatial clustering of applications with noise-based environment extraction method. When comparing the methods, our proposed method offers the advantage of extracting a more accurate environment without requiring parameter adjustments, even when the environment is changed. [ABSTRACT FROM AUTHOR]

Acquiring pulse signals of radar source is an essential component in implementing electronic support measures (ESM). The conventional signal detection or deinterleaving method are mainly applied in relatively simple environments. Currently, radar electronic reconnaissance signal processing capability is severely constrained by poor signal-to-noise ratio (SNR) and the interleaving of signals from various radar sources. This research develops a multi-layer particle swarm optimization (PSO) pulse extraction and deinterleaving technique to improve ESM's efficacy further. First, coherent accumulation of the received signals is performed using PSO to obtain higher SNR pulse. Second, the signals from this radar source are deinterleaved using the obtained pulse. Ultimately, the aforementioned procedures are combined into a multi-layer PSO architecture to capture radar source signals and deinterleaving them at low SNRs. The suggested algorithm's efficacy and robustness are confirmed through simulation experiments. [ABSTRACT FROM AUTHOR]

Traditional radar detection methods heavily rely on the signal-to-clutter ratio (SCR); a variety of feature-based detection methods have been proposed, providing a new way for radar detection and the recognition of weak targets. Existing feature-based detection methods determine the presence or absence of a target based on whether the feature value is within the judgment region, generally focusing only on the distribution of features and making insufficient use of inter-feature chronological information. This paper uses the autoregressive (AR) model to model and predict the time sequence of radar echoes in the feature domain and takes the chronological information of historical frame features as the prior information to form new features for detection on this basis. A classification method for floating small targets based on the Doppler spectrum centroid sequence is proposed. By using the AR model to fit the Doppler spectrum centroid feature sequence of the target, the model coefficients are regarded as the secondary features for target identification. The measured data show that the correct classification and identification rate of this method for ship targets and floating small targets can reach over 92% by using 50 centroid features. [ABSTRACT FROM AUTHOR]

The tropical forests in the Amazon store large amounts of carbon and are still considered a carbon sink. There is evidence that deforestation can turn a forest landscape into a carbon source due to land use and forest degradation. Deforestation causes fragmented forest landscapes. It is known from field experiments that forest dynamics at the edge of forest fragments are altered by changes in the microclimate and increased tree mortality ("edge effects"). However, it is unclear how this will affect large fragmented forest landscapes, and thus the entire Amazon region. The aim of this study is to investigate different forest attributes in edge and core forest areas at high resolution, and thus to identify the large-scale impacts of small-scale edge effects. Therefore, a well-established framework combining forest modelling and lidar-generated forest structure information was combined with radar-based forest cover data. Furthermore, forests were also analyzed at the landscape level to investigate changes between highly fragmented and less-fragmented landscapes. This study found that the aboveground biomass in forest edge areas is 27% lower than in forest core areas. In contrast, the net primary productivity is 13% higher in forest edge areas than in forest core areas. In the second step, whole fragmented landscapes were analyzed. Nearly 30% of all forest landscapes are highly fragmented, particularly in the regions of the Arc of Deforestation, on the edge of the Andes and on the Amazon river banks. Less-fragmented landscapes are mainly located in the central Amazon rainforest. The aboveground biomass is 28% lower in highly fragmented forest landscapes than in less-fragmented landscapes. The net primary productivity is 13% higher in highly fragmented forest landscapes than in less-fragmented forest landscapes. In summary, fragmentation of the Amazon rainforest has an impact on forest attributes such as biomass and productivity, with mostly negative effects on forest dynamics. If deforestation continues and the proportion of highly fragmented forest landscapes increase, the effect may be even more intense. By combining lidar, radar and forest modelling, this study shows that it is possible to map forest structure, and thus the degree of forest degradation, over a large area and derive more detailed information about the carbon dynamics of the Amazon region. [ABSTRACT FROM AUTHOR]

With the development of millimeter-wave radar systems, substrate-integrated waveguide (SIW) slot antennas have been widely used. This paper presents a computational approach for calculating the equivalent normalized admittance of slot in low sidelobe SIW longitudinal slot antennas. In contrast to the existing methods, this method takes into account the impact of the N-slots coupling on the admittance. Through this method, fitting functions for resonant length and conductance are obtained, which describe the relationship between the resonant length, resonant conductance, and offset of slots. Moreover, based on the two fitting functions, a low sidelobe SIW longitudinal slot linear antenna is designed for millimeter-wave radar applications at 77 GHz. This antenna consists of a transition from the standard WR-12 waveguide to the SIW and a set of radiating slots. The simulation results indicate an impedance bandwidth of 4.8% and a gain of 13.2 dBi. The sidelobe level in the H-plane is below −28 dB, which meets the desired specifications and confirms the accuracy of the presented method. [ABSTRACT FROM AUTHOR]

Gust fronts (GFs) belong to the boundary layer convergence system. A strong GF can cause serious wind disasters, so its automatic monitoring and identification are very helpful but difficult in daily meteorological operations. By collecting convective weather processes in Hubei, Jiangsu, and other regions of China, 1422 GFs from 106 S-band new-generation weather radar (CINRAD/SA) volume scan data are labeled as positive samples by means of human–computer interaction, and the same number of negative samples are randomly tagged from no GF radar data. A deep learning dataset including 2844 labels with a positive and negative sample ratio of 1:1 is constructed, and 80%, 10%, and 10% of the dataset are separated as training, validation, and test sets, respectively. Then, the training dataset is expanded to 273,120 samples by data augmentation technology. Since the height of a GF is generally less than 1.5 km, three deep-learning-based models are trained for GF automatic recognition according to the distance from the radars. Three models (M1, M2, M3) are trained with the data at a 0.5° elevation angle from 65 to 180 km away from the radars, at 0.5° and 1.5° angles from 40 to 65 km, and at 0.5°, 1.5°, and 2.4° angles within 40 km, respectively. The precision, confusion matrix, and its derived indicators including receiver operating characteristic curve (ROC) and the area under ROC (AUC) are used to evaluate the three models by the test set. The results show that the identification precisions of the models are 97.66% (M1), 90% (M2), and 90.43% (M3), respectively. All the hit rates are over 89%, the false positive rates are less than 11%, and the critical success indexes (CSIs) surpass 82%. In addition, all the optimal critical points on the ROC curves are close to (0, 1), and the AUC values are above 0.93. These results suggest that the three models can effectively achieve the automatic discrimination of GFs. Finally, the models are demonstrated by three GF events detected with Qingpu, Nantong, and Cangzhou radars. [ABSTRACT FROM AUTHOR]

Radar systems are a type of sensor that detects radio signals reflected from objects located a long distance from transmitters. For covering a longer range and a higher resolution in the operation of a radar, a high-frequency band and an array antenna are measures to take. Given a limited size to the antenna aperture in the front end of the radar, the choice of a millimeter-wave band leads to a denser layout for the array antenna and a higher antenna gain. Millimeter-wave signals tend to become attenuated faster by a larger loss of the covering material like the radome, implying this disadvantage offsets the advantage of high antenna directivity, compared to the C-band and X-band ones. As the radome is essential to the radar system to protect the array antenna from rain and dust, a metamaterial surface in the layer is suggested to meet multiple objectives. Firstly, the proposed electromagnetic structure is the protection layer for the source of radiation. Secondly, the metasurface does not disturb the millimeter-wave signal and makes its way through the cover layer to the air. This electromagnetically transparent surface transforms the phase distribution of the incident wave into the equal phase in the transmitted wave, resulting in an increased antenna gain. This is fabricated and assembled with the array antenna held in a 3D-printed jig with harnessing accessories. It is examined in view of S21 as the transfer coefficient between two ports of the VNA, having the antenna alone and with the metasurface. Additionally, the far-field test comes next to check the validity of the suggested structure and design. The bench test shows around a 7 dB increase in the transfer coefficient, and the anechoic chamber field test gives about a 5 dB improvement in antenna gain for a 24-band GHz array antenna. [ABSTRACT FROM AUTHOR]

The use of radar technology for non-contact measurement of vital parameters is increasingly being examined in scientific studies. Based on a systematic literature search in the PubMed, German National Library, Austrian Library Network (Union Catalog), Swiss National Library and Common Library Network databases, the accuracy of heart rate and/or respiratory rate measurements by means of radar technology was analyzed. In 37% of the included studies on the measurement of the respiratory rate and in 48% of those on the measurement of the heart rate, the maximum deviation was 5%. For a tolerated deviation of 10%, the corresponding percentages were 85% and 87%, respectively. However, the quantitative comparability of the results available in the current literature is very limited due to a variety of variables. The elimination of the problem of confounding variables and the continuation of the tendency to focus on the algorithm applied will continue to constitute a central topic of radar-based vital parameter measurement. Promising fields of application of research can be found in particular in areas that require non-contact measurements. This includes infection events, emergency medicine, disaster situations and major catastrophic incidents. [ABSTRACT FROM AUTHOR]