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Synthetic-Aperture Radar (SAR) has emerged as an important tool for monitoring tropical cyclones (TCs) due to its high spatial resolution and cloud-penetrating capability. Recent advancements in SAR technology have led to smaller and lighter satellites, yet few studies have evaluated their effectiveness in TC monitoring. This paper employs an algorithm for automatic TC center location, involving three stages: coarse estimation from a whole SAR image; precise estimation from a sub-SAR image; and final identification of the center using the lowest Normalized Radar Cross-Section (NRCS) value within a smaller sub-SAR image. Using three wide-swath miniaturized SAR images of TC Noru (2022), and TCs Doksuri and Koinu (2023), the algorithm's accuracy was validated by comparing estimated TC center positions with visually located data. For TC Noru, the distances for the three stages were 21.42 km, 14.39 km, and 8.19 km; for TC Doksuri—14.36 km, 20.48 km, and 17.10 km; and for TC Koinu—47.82 km, 31.59 km, and 5.42 km. The results demonstrate the potential of miniaturized SAR in TC monitoring. [ABSTRACT FROM AUTHOR]

The physical characteristics of electromagnetic waves are combined with digital information in coding metasurfaces. Coding metasurfaces enable precise control of beams by flexibly designing coding sequences. However, achieving continuous multivariate modulation of electromagnetic waves on passive flexible coded metasurfaces remains a challenge. Previous passive coding metasurfaces have a fixed phase difference between adjacent coding units throughout the operating frequency band, and when the coding pattern is defined, the coded metasurface can only achieve a single electromagnetic function. Our proposed frequency coding metasurface units vary linearly in phase difference over the operating frequency band with different phase sensitivities. Frequency coding metarsurfaces enable a wide range of tunable and versatile electromagnetic energy radiation, without introducing any active devices and changing the coding pattern. As a demonstration of the concept, we have shown theoretically and numerically that frequency coding metasurface can achieve successive transformations of electromagnetic functions, including multi-beam generation, anomalous deflection and diffuse scattering. In addition, beam sweeping function is achieved by means of spatially non-periodically distributed frequency coding metasurface. When the frequency of the incident wave is changed, the deflection angle of the beam is also changed. In addition to the tunability of properties, research on coding metasurfaces has tended to be limited to rigid materials. Flexible coding metasurfaces have potential applications in microwave antennas, radar and aircraft. The passive flexible frequency coding metasurfaces provide a novel approach to manipulating electromagnetic waves with increased design flexibility. This promises applications in microwave antennas, radar, aircraft, and satellite communications. [ABSTRACT FROM AUTHOR]

Radar signal intra-pulse modulation recognition can be addressed with convolutional neural networks (CNNs) and time–frequency images (TFIs). However, current CNNs have high computational complexity and do not perform well in low-signal-to-noise ratio (SNR) scenarios. In this paper, we propose a lightweight CNN known as the cross-scale aware network (CSANet) to recognize intra-pulse modulation based on three types of TFIs. The cross-scale aware (CSA) module, designed as a residual and parallel architecture, comprises a depthwise dilated convolution group (DDConv Group), a cross-channel interaction (CCI) mechanism, and spatial information focus (SIF). DDConv Group produces multiple-scale features with a dynamic receptive field, CCI fuses the features and mitigates noise in multiple channels, and SIF is aware of the cross-scale details of TFI structures. Furthermore, we develop a novel time–frequency fusion (TFF) feature based on three types of TFIs by employing image preprocessing techniques, i.e., adaptive binarization, morphological processing, and feature fusion. Experiments demonstrate that CSANet achieves higher accuracy with our TFF compared to other TFIs. Meanwhile, CSANet outperforms cutting-edge networks across twelve radar signal datasets, providing an efficient solution for high-precision recognition in low-SNR scenarios. [ABSTRACT FROM AUTHOR]

When it comes to road environment perception, millimeter-wave radar with a camera facilitates more reliable detection than a single sensor. However, the limited utilization of radar features and insufficient extraction of important features remain pertinent issues, especially with regard to the detection of small and occluded objects. To address these concerns, we propose a camera-radar fusion with radar channel extension and a dual-CBAM-FPN (CRFRD), which incorporates a radar channel extension (RCE) module and a dual-CBAM-FPN (DCF) module into the camera-radar fusion net (CRF-Net). In the RCE module, we design an azimuth-weighted RCS parameter and extend three radar channels, which leverage the secondary redundant information to achieve richer feature representation. In the DCF module, we present the dual-CBAM-FPN, which enables the model to focus on important features by inserting CBAM at the input and the fusion process of FPN simultaneously. Comparative experiments conducted on the NuScenes dataset and real data demonstrate the superior performance of the CRFRD compared to CRF-Net, as its weighted mean average precision (wmAP) increases from 43.89% to 45.03%. Furthermore, ablation studies verify the indispensability of the RCE and DCF modules and the effectiveness of azimuth-weighted RCS. [ABSTRACT FROM AUTHOR]

Non-cooperative targets, such as birds and unmanned aerial vehicles (UAVs), are typical low-altitude, slow, and small (LSS) targets with low observability. Radar observations in such scenarios are often complicated by strong motion clutter originating from sources like airplanes and cars. Hence, distinguishing between birds and UAVs in environments with strong motion clutter is crucial for improving target monitoring performance and ensuring flight safety. To address the impact of strong motion clutter on discriminating between UAVs and birds, we propose a frequency correlation dual-SVD (singular value decomposition) reconstruction method. This method exploits the strong power and spectral correlation characteristics of motion clutter, contrasted with the weak scattering characteristics of bird and UAV targets, to effectively suppress clutter. Unlike traditional clutter suppression methods based on SVD, our method avoids residual clutter or target loss while preserving the micro-motion characteristics of the targets. Based on the distinct micro-motion characteristics of birds and UAVs, we extract two key features: the sum of normalized large eigenvalues of the target's micro-motion component and the energy entropy of the time–frequency spectrum of the radar echoes. Subsequently, the kernel fuzzy c-means algorithm is applied to classify bird and UAV targets. The effectiveness of our proposed method is validated through results using both simulation and experimental data. [ABSTRACT FROM AUTHOR]

To address the challenges in recognizing various types of falls, which often exhibit high similarity and are difficult to distinguish, this paper proposes a human fall classification system based on the SE-Residual Concatenate Network (SE-RCNet) with adaptive weighted fusion. First, we designed the innovative SE-RCNet network, incorporating SE modules after dense and residual connections to automatically recalibrate feature channel weights and suppress irrelevant features. Subsequently, this network was used to train and classify three types of radar images: time–distance images, time–distance images, and distance–distance images. By adaptively fusing the classification results of these three types of radar images, we achieved higher action recognition accuracy. Experimental results indicate that SE-RCNet achieved F1-scores of 94.0%, 94.3%, and 95.4% for the three radar image types on our self-built dataset. After applying the adaptive weighted fusion method, the F1-score further improved to 98.1%. [ABSTRACT FROM AUTHOR]

This document introduces the RadIOCD, which is a dataset that contains sparse point cloud representations of indoor objects, collected by subjects wearing a commercial off-the-shelf mmWave radar. In particular, RadIOCD includes the recordings of 10 volunteers moving towards 5 different objects (i.e., backpack, chair, desk, human, and wall), placed in 3 different environments. RadIOCD includes sparse 3D point cloud data, together with their doppler velocity and intensity provided by the mmWave radar. A total of 5,776 files are available, with each one having an approximate duration of 8s. The scope of RadIOCD is the availability of data for the recognition of objects solely recorded by the mmWave radar, to be used in applications were the vision-based classification is cumbersome though critical (e.g., in search and rescue operation where there is smoke inside a building). Furthermore, we showcase that this dataset after being segmented into 76,821 samples contains enough data to apply Machine Learning-based techniques, ensuring that they could generalize in different environments and "unseen" subjects. [ABSTRACT FROM AUTHOR]

The Southern Ocean (SO) provides a unique natural laboratory for studying cloud formation and cloud-aerosol interactions with minimal anthropogenic influence. The Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES), was an aircraft-based campaign conducted from January 15 to February 28, 2018, off the coast of Hobart, Tasmania. During SOCRATES, the NSF/NCAR GV research aircraft, equipped with in-situ probes and remote sensors, observed aerosol, cloud, and precipitation properties, and provided detailed vertical structure of clouds over the SO, particularly for the low-level clouds (below 3 km). The HIAPER Cloud Radar (HCR) and in-situ cloud and drizzle probes (CDP and 2DS) measurements were used to provide comprehensive statistical and phase-relevant macrophysical properties for the low-level clouds sampled by the 15 research flights during SOCRATES. A new method based on HCR reflectivity and spectrum width gradient was developed to estimate cloud boundaries (cloud-base and -top heights) and classify cloud types based on their top and base heights. Low-level clouds were found to be the most prevalent, with an almost 90 % occurrence frequency. A new phase determination method was also developed to identify the single-layered low-level clouds as liquid, ice, and mixed phases, with occurrence frequencies of 45.4 %, 32.5 %, and 22.2 %, respectively. Low-level clouds over the SO have significantly higher SLW concentrations, with liquid being most prevalent at higher temperatures, ice phase dominating at lower temperatures, and mixed-phase being least common due to its thermodynamic instability. Regarding their vertical distributions, the liquid phase occurs most frequently in the lower mid-cloud range (from 500 m to 1 km), the mixed phase dominates at cloud bases lower than 1 km but is well distributed along the vertical cloud layer, while the ice phase is prevalent from the middle to upper cloud levels (1–3 km). The higher occurrence of the mixed phase at the cloud base could be attributed to large drizzle-sized drops and/or ice particles. [ABSTRACT FROM AUTHOR]

On August 8 and 9, 2022, a record-breaking rain rate of 142 mm h−1, with an accumulated rainfall of more than 500 mm, was observed in the Seoul metropolitan area, Republic of Korea. This study focuses on analyzing the concentration of lightning in southern Seoul, which occurred solely on August 8. It is worth noting that the daily rainfall of August 8 was approximately twice that of August 9 (381 mm on August 8 vs. 198 mm on August 9). The RKSG (located in Yongin, 40 km south of Seoul) Weather Surveillance Radar-1988 Doppler was used to explore the characteristics of cloud microphysics associated with lightning activity. Four major heavy rain periods on August 8 were grouped into three categories of lightning rate (e.g., intense, moderate, and none), and their polarimetric signatures were compared. Significant differences in the vertical distribution of graupel were found within the temperature range of 0 °C and − 20 °C, as indicated by radar reflectivity (ZH) > 40 dBZ and differential reflectivity (ZDR) < 0.5 dB. Although graupel was detected in all three categories at the relatively warm temperatures of 0 °C to − 10 °C, its presence extended into colder regions exclusively in the intense category. This observation preceded the appearance of lightning by approximately 6 min. At heights with temperature ≤ − 20 °C, a high concentration of vertically aligned ice crystals was observed in lightning-prone regions, leading to a decrease in differential phase (ΦDP). In summary, this study provides valuable insights into the microphysical characteristics of thunderstorms and their relationship to lightning activity in the Seoul metropolitan area. [ABSTRACT FROM AUTHOR]

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Based on C-band weather radar and ground precipitation data from the Helan Mountain area in Yinchuan between 2017 to 2020, we evaluated the forecasting performances of 15 mainstream deep learning models used in recent years, including recurrent-based and recurrent-free models. The critical success index (CSI), probability of detection (POD), false alarm rate (FAR), mean square error (MSE), mean absolute error (MAE), and learned perceptual image patch similarity (LPIPS), were used to evaluate the forecasting abilities. The results showed that (1) recurrent-free models have significant parameter quantity and computing power advantages, especially the SimVP model. Among the recurrent-based models, PredRNN and PredRNN++ demonstrate good predictive capabilities for changes in echolocation and intensity, PredRNN++ performs better in predicting long sequences (1 h); (2) SimVP uses Inception to extract temporal features, which cannot capture the complex physical changes in radar echo images and fails to extract spatial–temporal correlations and accurately predict heavy rainfall areas effectively. Therefore, we constructed the SimVP-GMA model, replacing the temporal prediction module in SimVP and modifying the spatial encoder part. Compared with SimVP, the MSE and LPIPS indicators were improved by 0.55 and 0.0193, respectively. It can be seen from the forecast images that the forecast details have been significantly improved, especially in the forecasting of heavy rainfall weather. [ABSTRACT FROM AUTHOR]

We propose a 10‐GHz 2 × 2 phased‐array radio frequency (RF) receiver with an 8‐bit linear phase and 15‐dB gain control range using 65‐nm complementary metal–oxide–semiconductor technology. An 8 × 8 phased‐array receiver module is implemented using 16 2 × 2 RF phased‐array integrated circuits. The receiver chip has four single‐to‐differential low‐noise amplifier and gain‐controlled phase‐shifter (GCPS) channels, four channel combiners, and a 50‐Ω driver. Using a novel complementary bias technique in a phase‐shifting core circuit and an equivalent resistance‐controlled resistor–inductor–capacitor load, the GCPS based on vector–sum structure increases the phase resolution with weighting‐factor controllability, enabling the vector–sum phase‐shifting circuit to require a low current and small area due to its small 1.2‐V supply. The 2 × 2 phased‐array RF receiver chip has a power gain of 21 dB per channel and a 5.7‐dB maximum single‐channel noise‐figure gain. The chip shows 8‐bit phase states with a 2.39° root mean‐square (RMS) phase error and a 0.4‐dB RMS gain error with a 15‐dB gain control range for a 2.5° RMS phase error over the 10 to10.5‐GHz band. [ABSTRACT FROM AUTHOR]

Three high-frequency (HF) ocean radars were installed at the Soya/La Perouse Strait to monitor the surface current fields of the Soya Warm Current (SWC), which flows through the strait from the Sea of Japan to the Sea of Okhotsk. These ocean radars were operated for 19 years, from August 2003 to March 2022. In this paper, seasonal to interannual variations in the SWC were investigated using surface current fields obtained by the HF ocean radars combined with coastal tide gauge records and sea level anomaly from satellite altimetry. The HF ocean radars clearly captured the seasonal and interannual variations in the SWC. The alongshore surface velocity of SWC showed a high correlation with the alongshore sea level difference (SLD) between the Sea of Japan and the Sea of Okhotsk if seasonal variations were included. However, anomalies of the SLD and SWC alongshore velocity exhibited a lower correlation, especially in summer. This suggests that different mechanisms may be involved in summer and winter. The alongshore SLD between Wakkanai and Abashiri, which has been utilized in previous studies as an indicator of SWC intensity, may not be appropriate for representing interannual variations in the SWC intensity in summer, when the SWC reaches the maximum current velocity. Instead, it was demonstrated that the offshore SLD anomaly estimated using satellite altimetry with the coastal tide gauge record at Wakkanai could be a better index for representing the interannual variations throughout the year. [ABSTRACT FROM AUTHOR]

Traditional radar systems use fixed patterns and constant electromagnetic wave transmission to illuminate targets, but they often do not effectively use prior information about targets and consume significant radar resources. Cognitive radar has emerged as a way to improve resource efficiency and address these shortcomings. A joint waveform design and resource allocation strategy for cognitive radar that incorporates target situational awareness is proposed. This method integrates the interacting multiple model algorithm and the Unscented Kalman Particle Filter to achieve target situation awareness as prior knowledge. By combining the target attitude and the frequency response function of the target radar cross section at different time points in the prior knowledge, a joint beam control and power allocation strategy is formulated and transformed into an optimization problem. In addition, a cognitive pulse‐to‐pulse frequency agile waveform design method is proposed to support multiple target tracking under complex motion models. Simulation experiments demonstrate the effectiveness of this approach in obtaining accurate target situation information, achieving beam control, and optimizing power allocation. The designed waveforms can enhance radar target detection performance and improve low probability of intercept characteristics by adjusting the pulse repetition interval. This method has significant technical value. [ABSTRACT FROM AUTHOR]

In tracking scenarios involving groups with dense targets, achieving effective data association is challenging due to mutual occlusion and interference among targets. The complexity of the tracking problem is further exacerbated in low‐observable environments by the increase in false alarm rates. The track‐before‐detect (TBD) is an advanced technology for detecting and tracking low‐observable targets, effectively mitigating data association problems by integrating multi‐frame echo data. However, the existing multi‐target TBD algorithms typically assume that the targets are spatially separated and are not suitable for scenarios involving group targets. A group target maximum‐likelihood probabilistic data association (GT‐ML‐PDA) algorithm, based on the concept of TBD, is proposed to track group targets effectively in low‐observable environments. The proposed algorithm divides group target tracking into two stages: group centre trajectory estimation and individual target trajectory estimation. To enhance the performance of the proposed algorithm, two strategies are suggested: modifying the equivalent measurements and extracting independent measurement sets for individual targets. Simulation results demonstrate that the proposed algorithm is capable of effectively tracking numerous individual targets within a group, even in the presence of heavy clutter. [ABSTRACT FROM AUTHOR]

Precise radar pulse detection is the premise of electronic support measures. A constant false alarm rate (CFAR) detection algorithm based on difference of box (DOB) filter is proposed to realise low‐complexity and environment‐adaptive radar pulse detection, and the principle behind the proposed algorithm is revealed. Specifically, the DOB filter is adopted to extract the rising edges and falling edges of radar pulses, and the signal variation law of probability density functions (PDFs) during the process of DOB filter are first theoretically analysed. Based on the derived PDFs, dynamic detection threshold and fixed threshold factor for the proposed CFAR algorithm based on DOB filter are deduced to detect the presence of radar pulses. Furthermore, the closed‐form expression of the detection probability is derived. Simulations and on‐board field programmable gate arrays implementation verify the superiority of the proposed CFAR algorithm based on DOB filter. Simulation results show that the proposed algorithm performs well in detecting various types of pulse signals, the detection probability ≥99% on condition that the signal‐to‐noise ratio ≥3 dB and false alarm probability = 10−8. Moreover, the proposed CFAR algorithm based on DOB filter can deal with both singular pulses and pulse on pulse signals. [ABSTRACT FROM AUTHOR]

Radar High‐Resolution Range Profile (HRRP) has great potential for target recognition because it can provide target structural information. Existing work commonly applies deep learning to extract deep features from HRRPs and achieve impressive recognition performance. However, most approaches are unable to distinguish between the target and non‐target regions in the feature extraction process and do not fully consider the impact of background noise, which is harmful to recognition, especially at low signal‐to‐noise ratios (SNR). To tackle these problems, the authors propose a radar HRRP target recognition framework termed Adaptive Soft Threshold Transformer (ASTT), which is composed of a patch embedding (PE) layer, ASTT blocks, and Discrete Wavelet Patch Merging (DWPM) layers. Given the limited semantic information of individual range cells, the PE layer integrates nearby isolated range cells into semantically explicit target structure patches. Thanks to its convolutional layer and attention mechanism, the ASTT blocks assign a weight to each patch to locate the target areas in the HRRP while capturing local features and constructing sequence correlations. Moreover, the ASTT block efficiently filters noise features in combination with a soft threshold function to further enhance the recognition performance at low SNR, where the threshold is adaptively determined. Utilising the reversibility of the discrete wavelet transform, the DWPM layer efficiently eliminates the loss of valuable information during the pooling process. Experiments based on simulated and measured datasets show that the proposed method has excellent target recognition performance, noise robustness, and small‐scale range shift robustness. [ABSTRACT FROM AUTHOR]

This paper describes a calibration procedure for a non-optimally configured High Frequency Radar (HFR) for the period 1 April 2021, to 31 March 2022, to assess sea waves characteristics. The HFR system, a 16.5 MHz WEllen RAdar (WERA), is part of an innovative network for monitoring the state of the sea. The system is installed in the western part of Sicily (Italy) where a wave buoy is positioned. HFR data underestimate the spectral significant wave heights (Hm0), in particular for Hm0 > 2 m, highlighting the need for calibration of the HFR system to ensure its optimal performance for operational purposes. The calibration was performed with both in-situ and modelled data provided by the Copernicus Marine Service. The best results were obtained when the buoy data were used as reference. Encouraging results were achieved as demonstrated by the improvement of the quantitative metrics after the calibration. Indeed, the RMSE decreased from 0.60 to 0.36 m; the correlation R increased slightly from 0.86 to 0.88, the slope from 0.48 to 0.8; whereas intercept from 0.11 to 0.31 m. Moreover, waves higher than > 2 m are well reproduced by the calibrated HFR time series with the RMSE decreasing from 1.3 to 0.53 m. [ABSTRACT FROM AUTHOR]

The Radar for Europa Assessment and Sounding: Ocean to Near-surface (REASON) is a dual-frequency ice-penetrating radar (9 and 60 MHz) onboard the Europa Clipper mission. REASON is designed to probe Europa from exosphere to subsurface ocean, contributing the third dimension to observations of this enigmatic world. The hypotheses REASON will test are that (1) the ice shell of Europa hosts liquid water, (2) the ice shell overlies an ocean and is subject to tidal flexing, and (3) the exosphere, near-surface, ice shell, and ocean participate in material exchange essential to the habitability of this moon. REASON will investigate processes governing this material exchange by characterizing the distribution of putative non-ice material (e.g., brines, salts) in the subsurface, searching for an ice–ocean interface, characterizing the ice shell's global structure, and constraining the amplitude of Europa's radial tidal deformations. REASON will accomplish these science objectives using a combination of radar measurement techniques including altimetry, reflectometry, sounding, interferometry, plasma characterization, and ranging. Building on a rich heritage from Earth, the moon, and Mars, REASON will be the first ice-penetrating radar to explore the outer solar system. Because these radars are untested for the icy worlds in the outer solar system, a novel approach to measurement quality assessment was developed to represent uncertainties in key properties of Europa that affect REASON performance and ensure robustness across a range of plausible parameters suggested for the icy moon. REASON will shed light on a never-before-seen dimension of Europa and – in concert with other instruments on Europa Clipper – help to investigate whether Europa is a habitable world. [ABSTRACT FROM AUTHOR]

Recent advances in radar seeker technologies have considerably improved missile precision and efficacy during target interception. This is especially concerning in the arenas of protection and safety, where appropriate countermeasures against enemy missiles are required to ensure the protection of naval facilities. In this study, we present a reinforcement-learning-based strategy for deploying decoys to enhance the survival probability of a target ship against a missile threat. Our approach involves the coordinated operation of three decoys, trained using the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) and Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) algorithms. The decoys operate in a leader–follower dynamic with a circular formation to ensure effective coordination. We evaluate the strategy across various parameters, including decoy deployment regions, missile launch directions, maximum decoy speeds, and missile speeds. The results indicate that, decoys trained with the MATD3 algorithm demonstrate superior performance compared to those trained with the MADDPG algorithm. Insights suggest that our decoy deployment strategy, particularly when utilizing MATD3-trained decoys, significantly enhances defensive measures against missile threats. [ABSTRACT FROM AUTHOR]

Ubiquitous Radar has become an essential tool for preventing bird strikes at airports, where accurate target classification is of paramount importance. The working mode of Ubiquitous Radar, which operates in track-then-identify (TTI) mode, provides both tracking information and Doppler information for the classification and recognition module. Moreover, the main features of the target's Doppler information are concentrated around the Doppler main spectrum. This study innovatively used tracking information to generate a feature enhancement layer that can indicate the area where the main spectrum is located and combines it with the RGB three-channel Doppler spectrogram to form an RGBA four-channel Doppler spectrogram. Compared with the RGB three-channel Doppler spectrogram, this method increases the classification accuracy for four types of targets (ships, birds, flapping birds, and bird flocks) from 93.13% to 97.13%, an improvement of 4%. On this basis, this study integrated the coordinate attention (CA) module into the building block of the 34-layer residual network (ResNet34), forming ResNet34_CA. This integration enables the network to focus more on the main spectrum information of the target, thereby further improving the classification accuracy from 97.13% to 97.22%. [ABSTRACT FROM AUTHOR]

Change detection using SAR data has been an active topic in various applications. Because conventional change detection identifies signal changes in single-pol radar observations, they cannot separately detect different kinds of change associated with different ground parameters. In this study, we investigated the comprehensive use of dual-pol parameters and proposed a novel dual-pol-based change detection framework utilizing different dual-pol scatter-type indicators. To optimize the exploitation of dual-pol change information, we presented a two-step processing strategy that divides the multiclass change detection process into a binary detection step that identifies the presence of changes and the classification step that distinguishes the types of change. In the detection stage, each dual-pol parameter was considered as an independent information source. Assuming potential conflict between dual-pol parameters, a disjunctive combination of detection results from different dual-pol parameters was applied to obtain the final detection result. In the classification step, an unsupervised change classification strategy was proposed based on the change direction and magnitude of the dual-pol parameters within the change class. Experimental results exhibited significantly improved detectability across a wide change spectrum compared with previous dual-pol-based change detection approaches. They also demonstrated the possibility of distinguishing different semantic changes without in situ ground data. [ABSTRACT FROM AUTHOR]

The satellite–ground bistatic configuration, which uses geosynchronous synthetic aperture radar (GEO SAR) for illumination and ground equipment for reception, can achieve wide coverage, high revisit, and continuous illumination of interest areas. Based on the analysis of the signal characteristics of GEO satellite–ground bistatic SAR (GEO SG-BiSAR), it is found that the bistatic echo signal has problems of azimuth spectrum aliasing and 2-D spatial variability. Therefore, to overcome those problems, a novel SAR imaging method for a GEO SG-BiSAR system with severe azimuth spectrum aliasing and 2-D spatial variability is proposed. Firstly, based on the geometric configuration of the GEO SG-BiSAR system, the time-domain and frequency-domain expressions of the signal are derived in detail. Secondly, in order to avoid the increasing cost caused by traditional multi-channel reception technology and the processing burden caused by inter-channel errors, the azimuth deramping is executed to solve the azimuth spectrum aliasing of the signal under the special geometric structure of GEO SG-BiSAR. Thirdly, based on the investigation of azimuth and range spatial variability characteristics of GEO SG-BiSAR in the Range Doppler (RD) domain, the azimuth spatial variability correction strategy is proposed. The signal corrected by the correction strategy has the same migration characteristics as monostatic radar. Therefore, the traditional chirp scaling function (CSF) is also modified to solve the range spatial variability of the signal. Finally, the two-dimensional spectrum of GEO SG-BiSAR with modified chirp scaling processing is derived, followed by the SPECAN operation to obtain the focused SAR image. Furthermore, the completed flowchart is also given to display the main composed parts for GEO SG-BiSAR imaging. Both azimuth spectrum aliasing and 2-D spatial variability are taken into account in the imaging method. The simulated data and the real data obtained by the Beidou navigation satellite are used to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Interrupted-sampling repeater jamming (ISRJ) is widely used in the field of electronic countermeasures, and can severely affect radar detection. Therefore, the problem of ISRJ suppression is a compelling task. In this paper, we propose an ISRJ suppression method based on an intrapulse frequency-coded joint frequency modulation (FM) slope agile waveform. The intrapulse frequency-coded joint FM slope agile waveform is first designed. The delay inserted between subpulses makes the waveform easy to implement in engineering, and the ambiguity function diagram of the waveform approximates the ideal thumbtack type. Next, the echo slices are classified in the fractional domain utilizing the discontinuity of ISRJ and the focusing property of fractional Fourier transform for chirp signals. Then, the target and interference in the interfered echo slices are reconstructed by compressed sensing, and a time-domain filter is constructed based on interference-free echo slices. Finally, the echo signal after interference suppression is further filtered in the time domain to degrade range sidelobes. Simulation results show that the proposed method can effectively suppress three typical types of ISRJ. Moreover, the probability of target detection after interference suppression exceeds 90% when the jamming-to-signal ratio equals 50 dB. [ABSTRACT FROM AUTHOR]

In detecting hypersonic vehicles, the radar echo signal is coupled with an intra-pulse Doppler frequency (I-D frequency) component caused by relative motion of a plasma sheath (PSh) and the vehicle, which can induce the phenomenon of a ghost target in a one-dimensional range profile. In order to investigate the I-D frequency generated by the relative motion of a PSh, this study transforms a linear frequency modulated (LFM) signal into a single carrier frequency signal based on echo signal equivalent time delay-dechirp processing and realizes high resolution and fast extraction of the I-D frequency coupled with the frequency-domain echo signal. Furthermore, by relying on the computation of the surface flow field of the RAMC-II Blunt Cone Reentry Vehicle, the coupled I-D frequency in the radar echo signal of a PSh-enveloped target under circumstances of typical altitudes and carrier frequencies is extracted and further investigated, revealing the variation law of I-D frequency. The key findings of this study provide a novel approach for suppressing anomalies in radar detection of PSh-enveloped targets as well as effective detecting and as robust target tracking. [ABSTRACT FROM AUTHOR]

Waveform design for integrated radar and jamming is generally based on the concept of shared waveform, which uses jamming signals without typical radar signal characteristics for detection. Existing waveforms have shown limited design flexibility, high levels of sidelobe in detection results, and overall ordinary performance. We propose an integrated radar and jamming waveform based on smart modulation and complementary coding. Unlike traditional integrated radar and jamming waveform based on smart modulation, the phase angle of the binary phase-coded sequence is adjustable in this smart modulation method, allowing for a controllable jamming effect, achieving true smart modulation. However, this smart modulation waveform also suffers from high sidelobes in detection. To address this issue, we take a complementary coding approach and design a smart modulation waveform with complementary characteristics. This waveform can synthesize a complete linear frequency modulation (LFM) signal by adding two pulses together, thereby reducing the sidelobes in the smart modulation waveform and enhancing its detection performance. Theoretical analysis indicates that the detection and jamming effects of this integrated waveform can be flexibly controlled by adjusting the phase angles of the coding sequences. Simulation analysis and experimental results confirm the significant advantages of this waveform. [ABSTRACT FROM AUTHOR]

For modern radar systems, small unmanned aerial vehicles (UAVs) belong to a typical types of targets with 'low, slow, and small' characteristics. In complex combat environments, the functional requirements of radar systems are not only limited to achieving stable detection and tracking performance but also to effectively complete the recognition of small UAV targets. In this paper, a multi-dimensional feature fusion framework for small UAV target recognition utilizing a small-sized and low-cost high-resolution radar is proposed, which can fully extract and combine the geometric structure features and the micro-motion features of small UAV targets. For the performance analysis, the echo data of different small UAV targets was measured and collected with a millimeter-wave radar, and the dataset consists of high-resolution range profiles (HRRP) and micro-Doppler time–frequency spectrograms was constructed for training and testing. The effectiveness of the proposed method was demonstrated by a series of comparison experiments, and the overall accuracy of the proposed method can reach 98.5%, which demonstrates that the proposed multi-dimensional feature fusion method can achieve better recognition performance than that of classical algorithms and higher robustness than that of single features for small UAV targets. [ABSTRACT FROM AUTHOR]

Due to suppression jamming by jammers, the signal-to-interference-plus-noise ratio (SINR) during tracking tasks is significantly reduced, thereby decreasing the target detection probability of radar systems. This may result in the interruption of the target track. To address this issue, we propose a multi-static radar power allocation algorithm that enhances the detection and tracking performance of multiple radars in relation to their targets by optimizing power resource allocation. Initially, the echo signal model and measurement model of multi-static radar are formulated, followed by the derivation of the Bayesian Cramér–Rao lower bound (BCRLB). The multi-objective optimization method is utilized to establish the objective function for joint tracking and detection, with dynamic adjustment of the weight coefficient to balance the tracking and detection performance of multiple radars. This ensures the reliability and anti-jamming capability of the multi-static radar system. Simulation results indicate that the proposed algorithm can prevent the interruption of jammer tracking and maintain robust tracking performance. [ABSTRACT FROM AUTHOR]

Clear-air echo studies are usually based on isotropic turbulence theory. But the theory has been considered incomplete by modern turbulence theory. The intermittence of turbulence can reveal obvious shortcomings in the existing studies of clear-air echoes. The mechanism of clear-air echo scattering needs to be supplemented. This paper introduces the troposcatter theory, normally used in over-the-horizon communication, to fill the gap left by Bragg scattering. By treating radar as a self-transmitted and self-received device, the equivalent transmission loss of weather radar is established and compared with the recommendations of the Radiocommunication Sector of the International Telecommunication Union (ITU-R). The results show that the S-band radar transmission loss aligns with ITU-R recommendations. There is also a linear regression relationship between the radar transmission loss and height, which conforms to the troposcatter theory. This means that the theory of troposcatter scattering is a supplement to the theory of Bragg scattering. Tropospheric scattering can be thought of as general Bragg scattering. Meanwhile, based on ITU-R recommendations, this study also provides a new way for the recognition of biological echoes. [ABSTRACT FROM AUTHOR]

Viscose-based carbon fibers (VBCFs) were chlorinated with CCl4 vapor to investigate the effect of chlorination temperature on their microwave properties. The maximum chlorine content of 2.0 mmol g−1 was obtained at 450 °C. TEM and SEM showed carbon nanostructures and surface morphology preservation, respectively. TGA and TPD MS confirmed the formation of carbon-oxygen and chlorine-carbon groups, including CCl3 groups. The prepared chlorinated VBCFs can effectively reflect X-band microwaves, with nearly constant reflection losses over a wide frequency range, and are characterized by minimum transmission losses of −22 dB. [ABSTRACT FROM AUTHOR]

More often, in the mathematical literature, the injectivity of the spherical Radon transform (SRT) for compactly supported functions is considered. In this article, an additional condition, for the reconstruction of an unknown function f∈C(R2)$f\in C(\mathbf {R}^2)$ (the support can be noncompact) using the circular Radon transform (CRT) over circles centered on a smooth simple curve is found. It is proved that this problem is equivalent to the injectivity of a so‐called two‐data CRT over circles centered on a smooth curve (can be a segment). Also, we present an inversion formula of the transform that uses the local data of the circular integrals to reconstruct the unknown function. Such inversions are the mathematical base of modern modalities of imaging, such as thermo‐ and photoacoustic tomography and radar imaging, and have theoretical significance. [ABSTRACT FROM AUTHOR]

Accurate estimations of extreme precipitation return levels are critical for many hydrological applications. Extreme precipitation is highly variable in both space and time; therefore, to better understand and manage the related risks, knowledge of their probability at different spatial–temporal scales is crucial. We employ a novel non-asymptotic framework to estimate extreme return levels (up to 100 years) at multiple spatial–temporal scales from weather radar precipitation estimates. The approach reduces uncertainties and enables the use of relatively short archives typical of weather radar data (12 years in this case). We focus on the eastern Mediterranean, an area of high interest due to its sharp climatic gradient, containing Mediterranean, semi-arid, and arid areas across a few tens of kilometres, and its susceptibility to flash flood. At-site intensity–duration–area–frequency relations are derived from radar precipitation data at various scales (10 min–24 h, 0.25–500 km 2) across the study area, using ellipses of varying axes and orientations to account for the spatial component of storms. We evaluate our analysis using daily rain gauge data over areas for which sufficiently dense gauge networks are available. We show that extreme return levels derived from radar precipitation data for 24 h and 100 km 2 are generally comparable to those derived from averaging daily rain gauge data over a similar areal scale. We then analyse differences in multi-scale extreme precipitation over coastal, mountainous, and desert regions. Our study reveals that the power-law scaling relationship between precipitation and duration (simple scaling) weakens for increasing area sizes. This finding has implications for temporal downscaling. Additionally, precipitation intensity varies significantly for different area sizes at short durations but becomes more similar at long durations, suggesting that, in the region, areal reduction factors may not be necessary for computing return levels over long durations. Furthermore, the reverse orographic effect, which causes decreased precipitation for hourly and sub-hourly durations, diminishes for larger areas. Finally, we discuss the effects of orography and coastline proximity on extreme precipitation intensity over different spatial–temporal scales. [ABSTRACT FROM AUTHOR]

The paper presents new detailed radar maps and polarimetric data covering the southern polar region of the near side of the Moon measuring 400 × 800 km with a spatial resolution of about 75 m. The data were obtained using the 64-meter antenna (TNA-1500) of the Satellite Communications Center Bear Lakes of the Special Design Bureau of the Moscow Power Engineering Institute and the 13.2-meter radio telescope (RT-13) of the Svetloe Observatory of the Institute of Applied Astronomy, Russian Academy of Sciences, at a wavelength of 4.2 cm. At this wavelength, radar signals penetrate the lunar regolith to depths of up to 1 m and are sensitive to surface and suspended rocks larger than 1 cm. The maps show 39% of the area of permanently shadowed regions not observable by optical Earth-based instruments, which may hide water ice deposits. Analysis of radar maps did not reveal any relationship between the polarization properties of the surface in these regions and the presence of solar illumination. The data obtained as a result of this work can be used to study the surface and subsurface regolith features of the lunar south polar region, including searching for ice deposits in permanently shadowed regions, as well as for planning future lunar missions. [ABSTRACT FROM AUTHOR]

Gesture plays an important role in human–machine interaction. However, the insufficient accuracy and high complexity of gesture recognition have blocked its widespread application. A gesture recognition method that combines state machine and bidirectional long short-term memory (Bi-LSTM) fusion neural network is proposed to improve the accuracy and efficiency. Firstly, gestures with large movements are categorized into simple trajectory gestures and complex trajectory gestures in advance. Afterwards, different recognition methods are applied for the two categories of gestures, and the final result of gesture recognition is obtained by combining the outputs of the two methods. The specific method used is a state machine that recognizes six simple trajectory gestures and a bidirectional LSTM fusion neural network that recognizes four complex trajectory gestures. Finally, the experimental results show that the proposed simple trajectory gesture recognition method has an average accuracy of 99.58%, and the bidirectional LSTM fusion neural network has an average accuracy of 99.47%, which can efficiently and accurately recognize 10 gestures with large movements. In addition, by collecting more gesture data from untrained participants, it was verified that the proposed neural network has good generalization performance and can adapt to the various operating habits of different users. [ABSTRACT FROM AUTHOR]

This study investigated a frequency-selective surface (FSS) that is used to suppress harmonics affecting the performance and accuracy of radar systems. One side of the FSS features a metal grid structure, and when converted into an equivalent circuit model, it exhibits the characteristics of a band-pass filter with an L and C parallel structure. The other side of the FSS features a metallic loop structure and when represented as an equivalent circuit model, it exhibits the characteristics of a band-stop filter with an L and C series structure. The reflection coefficient ( S 11 ) and transmission coefficient ( S 21 ) of the FSS designed based on theory are compared using a CST studio suite and Keysight's Advanced Design System. In addition, the transmission coefficients are verified through actual measurements, wherein the measured transmission coefficient is −0.1 dB at 3.0 GHz and approximately −50 dB at the harmonic frequency of 6.0 GHz. The designed FSS is attached to an actual radar system, and the 2D radiation pattern and maximum gain are measured during steering in boresight, azimuth ( 30 ∘ ) and elevation ( 30 ∘ ) directions. At 3.0 GHz, the maximum gain in boresight is 17.25 dB without the FSS and 17.12 dB with the FSS. At 6.0 GHz, the maximum gain is 12.79 dB without the FSS and 2.69 dB with the FSS. At 3.0 GHz, the maximum gain during azimuth steering is 16.13 dB without the FSS and 16.68 dB with the FSS, and the maximum gain during elevation steering is 15.74 dB without the FSS and 15.90 dB with the FSS. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) printing technology has revolutionized the fabrication of complex geometries, including electromagnetic wave absorbers. In this study, the multilayer radar absorbing structure (RAS) was designed and fabricated using a continuous fiber 3D printing process based on material extrusion (ME). However, a large thickness error occurred during the 3D printing process. The thickness error in the multilayer RAS, which is a resonant electromagnetic wave absorber, causes a large error in the electromagnetic wave absorption performance. To address this issue, the thickness correction method based on unit thickness was proposed. The unit thickness, which is the basic thickness of the multilayer RAS, was calculated to account for the thickness increase during printing. The redesigned RAS based on the calculated unit thickness effectively corrected the thickness error. The total thickness and thickness of each layer of the corrected RAS were measured and found to be similar to the design value. The measured electromagnetic wave absorption performance closely matched the analyzed value, confirming the effectiveness of the unit thickness-based correction method in improving the ME-based 3D printing process for fabricating RAS. [ABSTRACT FROM AUTHOR]

In this paper, we propose an integrated sensing and communication (ISAC) base station (BS) system designed for applications by multiple users in complex offshore environments. We begin by modeling the communication and detection functions of the dual-function BS in the corresponding maritime channel environment, as well as the sea clutter influence of multiple-input-multiple-output (MIMO) radar in the marine environment. To evaluate the performance of our dual-function system, we measure the communication reachable rate and sensing signal-clutter-noise ratio (SCNR) of the offshore system separately for communication and radar functions, respectively. We then develop two optimization schemes to maximize the system's performance under power constraint of radar SCNR and communication rate constraint, respectively, and solve them using convex optimization methods such as the successive convex approximation (SCA) method. We also model the beamforming and provide simulation results that demonstrate the effectiveness of our proposed maritime integrated sensing and communication (MISAC) system. [ABSTRACT FROM AUTHOR]

Due to the alleviated spectral congestion through spectrum sharing, integrating radar and communication has raised increasing attention in both academic and industrial fields. This paper proposes several orthogonal frequency division multiplexing (OFDM) based waveform design approaches for joint cooperation of radar sensing and communication transmission. A mathematical model of comprehensive weight selection, communication modulation mode and phase coding sequence is proposed, aiming at the problems of high peak sidelobe and large envelope fluctuation in OFDM integrated waveform, two feasible schemes to achieve superior performance in terms of ambiguity function are investigated, including the iterative optimization of the peak sidelobe ratio (PSLR) and peak to average power ratio (PAPR) optimization method based on random phase coding, which can improve the range resolution without degrading Doppler resolution. Simulation results show that the proposed waveform optimization method can improve PSLR below −30dB and PAPR below 2, verifying the effectiveness of the designed integrated waveform in simultaneous radar and communication operations. [ABSTRACT FROM AUTHOR]

The anti-sorting transmission is one of the primary RF stealth signals, as well as radio frequency (RF) stealth is one of the greatest significant study areas in radar stealth technology. This study first examines the anti-sorting signal's design basis. Next, it is suggested to use a quadratic stochastic appropriate algorithm on the cosine exponent. Furthermore, utilizing the suggested nonlinear chaotic mapping, a more unpredictable wide-interval PRI signals is created while taking the sorting individual's allowable limits into account. Finally, evaluations of intricacy, equilibrium, and unpredictability confirm the effectiveness of the suggested chaotic mapping. The transmitter capacity for anti-sorting is then modeled. The outcomes demonstrate the intended signal's strong anti-sorting capabilities. [ABSTRACT FROM AUTHOR]

The unusual stripe‐like scattered echoes were observed by the Hainan Coherent Scatter Phased Array Radar (HCOPAR) in the nigh of 9 Sep. 2017. There were parallel stripes with the interval of ∼33 min appearing from 12:57 to 17:50 UT. While the emergence of the scattered echoes, the Hainan Digisonde has observed the Spread‐F, and the Total Electron Content (TEC) maps recorded by the Global Navigation Satellite System (GNSS) in China show that there was no Equatorial Plasma Bubble (EPB), but the Medium‐Scale Traveling Ionospheric Disturbance (MSTID) traveled southwestward. The spatial and temporal distributions of the stripe‐like echoes and the MSTID show great consistency, indicating that the F‐region Field‐Aligned Irregularities were generated in the wave peaks and troughs of the MSTID. The MSTID has decayed greatly while reaching the HCOPAR, and the echo pattern is determined by the wave features of the MSTID. Key Points: Unusual stripe‐like echoes scattered from nighttime F‐region Field‐Aligned Irregularities were observed at low‐latitudesThe stripe‐like echo pattern exhibits a good correlation with the Medium‐Scale Traveling Ionospheric Disturbance (MSTID) observed in the Total Electron Content mapsThe scattered echoes were generated in wave troughs and peaks of the MSTID, and the echo pattern is determined by the wave features [ABSTRACT FROM AUTHOR]

The spectacular visual displays from the aurora come from curtains of excited atoms and molecules, impacted by energetic charged particles. These particles are accelerated from great distances in Earth's magnetotail, causing them to precipitate into the ionosphere. Energetic particle precipitation is associated with currents that generate electric fields, and the end result is a dissipation of the hundreds of gigawatts to terrawatts of energy injected into Earth's atmosphere during geomagnetic storms. While much is known about how the aurora dissipates energy through Joule heating, little is known about how it does so via small‐scale plasma turbulence. Here we show the first set of combined radar and optical images that track the position of this turbulence, relative to particle precipitation, with high spatial precision. During two geomagnetic storms occurring in 2021, we unambiguously show that small‐scale turbulence (several meters) is preferentially created on the edges of auroral forms. We find that turbulence appears both poleward and equatorward of auroral forms, as well as being nestled between auroral forms in the north‐south direction. These measurements make it clear that small scale auroral plasma turbulence is an integral part of the electrical current system created by the aurora, in the sense that turbulent transport around auroral forms enhances ionospheric energy deposition through Joule heating while at the same time reducing the average strength of the electric field. Plain Language Summary: The aurora continuous to amaze the inhabitants and travelers of Earth's polar regions. Bright shifting folds of light extend down from the nightsky, appearing as green, red, or faint‐blue curtains. During geomagnetic storms the are particularly bright and dynamic, often visible in large parts of the inhabited globe. However, far from being simple displays of light, the aurora can wreak havoc on the thin gas of Earth's upper atmosphere. There, gigantic swirls of electric turbulence are excited in response to the energy that is being pumped into the atmosphere by the aurora. This plasma turbulence is detrimental to satellite communication, such as the principle operation of the GPS network, and future efforts are sorely needed to understand the when and how this turbulence appears. We present a series of photographical and radar‐based images of the aurora and its plasma turbulence, shedding light on the complex relationship between the two phenomena. The images and videos we present are accessible and interesting to a public readership. Key Points: Small‐scale auroral plasma turbulence is created preferably outside of but not far from optical auroral formsTurbulence appears both poleward and equatorward of auroral arcsStrong electric fields that trigger meter‐size E region turbulence are sometimes seen before the onset of optical aurora [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the requirement of radar wave absorption in automotive radar tests for both the 24 GHz and 77 GHz vehicle radar bands, two ultra-thin dual-band circuit analog absorber designs based on frequency-selective conductive film have been proposed. They are obtained by first optimising the equivalent circuit parameters to achieve the desired reflection response, then determining the physical parameters (geometrical size and square resistance) of unit cells from the equivalent circuit parameters, and finally fine-tuning these physical parameters. Both designs employ unit cell based on the nested structure of conductive square ring and a conductive square patch, which has the advantages of simple structure, ease of processing, high design freedom, and good polarisation and angular stability. The 1st design employs different square resistance values for the conductive ring and patch, while the 2nd design employs a single square resistance value for both conductive shapes. Simulation results show that, for each of the two designs, the lower and upper 70% absorption or −10 dB reflection bands simultaneously cover the 24 GHz and 77 GHz vehicle radar bands, respectively, with good angular stability. The dual-band coverage keeps stable until the elevation incident angle reaches 20° and 50° for the 1st and 2nd designs, respectively. A realistic sample is processed regarding the 2nd design and its dual-band absorption performance has been verified by experimental results, which have good agreement with the simulated counterparts. [ABSTRACT FROM AUTHOR]

Assessing sleep posture, a critical component in sleep tests, is crucial for understanding an individual's sleep quality and identifying potential sleep disorders. However, monitoring sleep posture has traditionally posed significant challenges due to factors such as low light conditions and obstructions like blankets. The use of radar technolsogy could be a potential solution. The objective of this study is to identify the optimal quantity and placement of radar sensors to achieve accurate sleep posture estimation. We invited 70 participants to assume nine different sleep postures under blankets of varying thicknesses. This was conducted in a setting equipped with a baseline of eight radars—three positioned at the headboard and five along the side. We proposed a novel technique for generating radar maps, Spatial Radio Echo Map (SREM), designed specifically for data fusion across multiple radars. Sleep posture estimation was conducted using a Multiview Convolutional Neural Network (MVCNN), which serves as the overarching framework for the comparative evaluation of various deep feature extractors, including ResNet-50, EfficientNet-50, DenseNet-121, PHResNet-50, Attention-50, and Swin Transformer. Among these, DenseNet-121 achieved the highest accuracy, scoring 0.534 and 0.804 for nine-class coarse- and four-class fine-grained classification, respectively. This led to further analysis on the optimal ensemble of radars. For the radars positioned at the head, a single left-located radar proved both essential and sufficient, achieving an accuracy of 0.809. When only one central head radar was used, omitting the central side radar and retaining only the three upper-body radars resulted in accuracies of 0.779 and 0.753, respectively. This study established the foundation for determining the optimal sensor configuration in this application, while also exploring the trade-offs between accuracy and the use of fewer sensors. [ABSTRACT FROM AUTHOR]

Short-term precipitation forecasting is essential for agriculture, transportation, urban management, and tourism. The radar echo extrapolation method is widely used in precipitation forecasting. To address issues like forecast degradation, insufficient capture of spatiotemporal dependencies, and low accuracy in radar echo extrapolation, we propose a new model: MS-DD3D-RSTN. This model employs spatiotemporal convolutional blocks (STCBs) as spatiotemporal feature extractors and uses the spatial-temporal loss (STLoss) function to learn intra-frame and inter-frame changes for end-to-end training, thereby capturing the spatiotemporal dependencies in radar echo signals. Experiments on the Sichuan dataset and the HKO-7 dataset show that the proposed model outperforms advanced models in terms of CSI and POD evaluation metrics. For 2 h forecasts with 20 dBZ and 30 dBZ reflectivity thresholds, the CSI metrics reached 0.538, 0.386, 0.485, and 0.198, respectively, representing the best levels among existing methods. The experiments demonstrate that the MS-DD3D-RSTN model enhances the ability to capture spatiotemporal dependencies, mitigates forecast degradation, and further improves radar echo prediction performance. [ABSTRACT FROM AUTHOR]