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Monitoring patient safety in high-risk mental health environments is a challenge for clinical staff. There has been a recent increase in the adoption of contactless sensing solutions for remote patient monitoring. mmWave radar is a technology that has high potential in this field due it its low cost and protection of privacy; however, it is prone to multipath reflections and other sources of environmental noise. This paper discusses some of the challenges in mmWave remote sensing applications for patient safety in mental health wards. In line with these challenges, we propose a novel low-data solution to mitigate the impact of multipath reflections and other sources of noise in mmWave sensing. Our solution uses an unscented Kalman filter for target tracking over time and analyses features of movement to determine whether targets are human or not. We chose a commercial off-the-shelf radar and compared the accuracy and reliability of sensor measurements before and after applying our solution. Our results show a marked decrease in false positives and false negatives during human target tracking, as well as an improvement in spatial location detection in a two-dimensional space. These improvements demonstrate how a simple low-data solution can improve existing mmWave sensors, making them more suitable for patient safety solutions in high-risk environments.

Recognizing sleep posture is crucial for the monitoring of people with sleeping disorders. Existing contact-based systems might interfere with sleeping, while camera-based systems may raise privacy concerns. In contrast, radar-based sensors offer a promising solution with high penetration ability and the capability to detect vital bio-signals. This study propose a deep learning method for human sleep pose recognition from signals acquired from single-antenna Frequency-Modulated Continuous Wave (FMCW) radar device. To capture both frequency features and sequential features, we introduce ResTCN, an effective architecture combining Residual blocks and Temporal Convolution Network (TCN) to recognize different sleeping postures, from augmented statistical motion features of the radar time series. We rigorously evaluated our method with an experimentally acquired data set which contains sleeping radar sequences from 16 volunteers. We report a classification accuracy of 82.74% on average, which outperforms the state-of-the-art methods.

Physiotherapy plays a crucial role in the rehabilitation of damaged or defective organs due to injuries or illnesses, often requiring long-term supervision by a physiotherapist in clinical settings or at home. AI-based support systems have been developed to enhance the precision and effectiveness of physiotherapy, particularly during the COVID-19 pandemic. These systems, which include game-based or tele-rehabilitation monitoring using camera-based optical systems like Vicon and Microsoft Kinect, face challenges such as privacy concerns, occlusion, and sensitivity to environmental light. Non-optical sensor alternatives, such as Inertial Movement Units (IMUs), Wi-Fi, ultrasound sensors, and ultrawide band (UWB) radar, have emerged to address these issues. Although IMUs are portable and cost-effective, they suffer from disadvantages like drift over time, limited range, and susceptibility to magnetic interference. In this study, a single UWB radar was utilized to recognize five therapeutic exercises related to the upper limb, performed by 34 male volunteers in a real environment. A novel feature fusion approach was developed to extract distinguishing features for these exercises. Various machine learning methods were applied, with the EnsembleRRGraBoost ensemble method achieving the highest recognition accuracy of 99.45%. The performance of the EnsembleRRGraBoost model was further validated using five-fold cross-validation, maintaining its high accuracy.

Falls among the elderly are a common and serious health risk that can lead to physical injuries and other complications. To promptly detect and respond to fall events, radar-based fall detection systems have gained widespread attention. In this paper, a deep learning model is proposed based on the frequency spectrum of radar signals, called the convolutional bidirectional long short-term memory (CB-LSTM) model. The introduction of the CB-LSTM model enables the fall detection system to capture both temporal sequential and spatial features simultaneously, thereby enhancing the accuracy and reliability of the detection. Extensive comparison experiments demonstrate that our model achieves an accuracy of 98.83% in detecting falls, surpassing other relevant methods currently available. In summary, this study provides effective technical support using the frequency spectrum and deep learning methods to monitor falls among the elderly through the design and experimental validation of a radar-based fall detection system, which has great potential for improving quality of life for the elderly and providing timely rescue measures.

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%.

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.

Radar systems are diverse and used in industries such as air traffic control, weather monitoring, and military and maritime applications. Within the scope of this study, we focus on using radar for human detection and recognition. This study evaluated the general state of micro-Doppler radar-based human recognition technology, the related literature, and state-of-the-art methods. This study aims to provide guidelines for new research in this area. This comprehensive study provides researchers with a thorough review of the existing literature. It gives a taxonomy of the literature and classifies the existing literature by the radar types used, the focus of the research, targeted use cases, and the security concerns raised by the authors. This paper serves as a repository for numerous studies that have been listed, critically evaluated, and systematically classified.

Radar sensors, leveraging the Doppler effect, enable the nonintrusive capture of kinetic and physiological motions while preserving privacy. Deep learning (DL) facilitates radar sensing for healthcare applications such as gait recognition and vital-sign measurement. However, band-dependent patterns, indicating variations in patterns and power scales associated with frequencies in time-frequency representation (TFR), challenge radar sensing applications using DL. Frequency-dependent characteristics and features with lower power scales may be overlooked during representation learning. This paper proposes an Enhanced Band-Dependent Learning framework (E-BDL) comprising an adaptive sub-band filtering module, a representation learning module, and a sub-view contrastive module to fully detect band-dependent features in sub-frequency bands and leverage them for classification. Experimental validation is conducted on two radar datasets, including gait abnormality recognition for Alzheimer's disease (AD) and AD-related dementia (ADRD) risk evaluation and vital-sign monitoring for hemodynamics scenario classification. For hemodynamics scenario classification, E-BDL-ResNet achieves competitive performance in overall accuracy and class-wise evaluations compared to recent methods. For ADRD risk evaluation, the results demonstrate E-BDL-ResNet's superior performance across all candidate models, highlighting its potential as a clinical tool. E-BDL effectively detects salient sub-bands in TFRs, enhancing representation learning and improving the performance and interpretability of DL-based models.

IoT sensors offer a wide range of sensing capabilities, many of which have potential health applications. Existing solutions for IoT in healthcare have notable limitations, such as closed-source, limited I/O protocols, limited cloud platform support, and missing specific functionality for health use cases. Developing an open-source internet of things (IoT) gateway solution that addresses these limitations and provides reliability, broad applicability, and utility is highly desirable. Combining a wide range of sensor data streams from IoT devices with ambulatory mHealth data would open up the potential to provide a detailed 360-degree view of the relationship between patient physiology, behavior, and environment. We have developed RADAR-IoT as an open-source IoT gateway framework, to harness this potential. It aims to connect multiple IoT devices at the edge, perform limited on-device data processing and analysis, and integrate with cloud-based mobile health platforms, such as RADAR-base, enabling real-time data processing. We also present a proof-of-concept data collection from this framework, using prototype hardware in two locations. The RADAR-IoT framework, combined with the RADAR-base mHealth platform, provides a comprehensive view of a user's health and environment by integrating static IoT sensors and wearable devices. Despite its current limitations, it offers a promising open-source solution for health research, with potential applications in managing infection control, monitoring chronic pulmonary disorders, and assisting patients with impaired motor control or cognitive ability.

Human action recognition based on optical and infrared video data is greatly affected by the environment, and feature extraction in traditional machine learning classification methods is complex; therefore, this paper proposes a method for human action recognition using Frequency Modulated Continuous Wave (FMCW) radar based on an asymmetric convolutional residual network. First, the radar echo data are analyzed and processed to extract the micro-Doppler time domain spectrograms of different actions. Second, a strategy combining asymmetric convolution and the Mish activation function is adopted in the residual block of the ResNet18 network to address the limitations of linear and nonlinear transformations in the residual block for micro-Doppler spectrum recognition. This approach aims to enhance the network's ability to learn features effectively. Finally, the Improved Convolutional Block Attention Module (ICBAM) is integrated into the residual block to enhance the model's attention and comprehension of input data. The experimental results demonstrate that the proposed method achieves a high accuracy of 98.28% in action recognition and classification within complex scenes, surpassing classic deep learning approaches. Moreover, this method significantly improves the recognition accuracy for actions with similar micro-Doppler features and demonstrates excellent anti-noise recognition performance.

Breathing is one of the body's most basic functions and abnormal breathing can indicate underlying cardiopulmonary problems. Monitoring respiratory abnormalities can help with early detection and reduce the risk of cardiopulmonary diseases. In this study, a 77 GHz frequency-modulated continuous wave (FMCW) millimetre-wave (mmWave) radar was used to detect different types of respiratory signals from the human body in a non-contact manner for respiratory monitoring (RM). To solve the problem of noise interference in the daily environment on the recognition of different breathing patterns, the system utilised breathing signals captured by the millimetre-wave radar. Firstly, we filtered out most of the static noise using a signal superposition method and designed an elliptical filter to obtain a more accurate image of the breathing waveforms between 0.1 Hz and 0.5 Hz. Secondly, combined with the histogram of oriented gradient (HOG) feature extraction algorithm, K-nearest neighbours (KNN), convolutional neural network (CNN), and HOG support vector machine (G-SVM) were used to classify four breathing modes, namely, normal breathing, slow and deep breathing, quick breathing, and meningitic breathing. The overall accuracy reached up to 94.75%. Therefore, this study effectively supports daily medical monitoring.

A 77 GHz frequency-modulated continuous wave (FMCW) radar was utilized to extract biomechanical parameters for gait analysis in indoor scenarios. By preprocessing the collected raw radar data and eliminating environmental noise, a range-velocity-time (RVT) data cube encompassing the subjects' information was derived. The strongest signals from the torso in the velocity and range dimensions and the enveloped signal from the toe in the velocity dimension were individually separated for the gait parameters extraction. Then, six gait parameters, including step time, stride time, step length, stride length, torso velocity, and toe velocity, were measured. In addition, the Qualisys system was concurrently utilized to measure the gait parameters of the subjects as the ground truth. The reliability of the parameters extracted by the radar was validated through the application of the Wilcoxon test, the intraclass correlation coefficient (ICC) value, and Bland-Altman plots. The average errors of the gait parameters in the time, range, and velocity dimensions were less than 0.004 s, 0.002 m, and 0.045 m/s, respectively. This non-contact radar modality promises to be employable for gait monitoring and analysis of the elderly at home.

Millimeter-wave radar-based identification technology has a wide range of applications in persistent identity verification, covering areas such as security production, healthcare, and personalized smart consumption systems. It has received extensive attention from the academic community due to its advantages of being non-invasive, environmentally insensitive and privacy-preserving. Existing identification algorithms mainly rely on a single signal, such as breathing or heartbeat. The reliability and accuracy of these algorithms are limited due to the high similarity of breathing patterns and the low signal-to-noise ratio of heartbeat signals. To address the above issues, this paper proposes an algorithm for multimodal fusion for identity recognition. This algorithm extracts and fuses features derived from phase signals, respiratory signals, and heartbeat signals for identity recognition purposes. The spatial features of signals with different modes are first extracted by the residual network (ResNet), after which these features are fused with a spatial-channel attention fusion module. On this basis, the temporal features are further extracted with a time series-based self-attention mechanism. Finally, the feature vectors of the user's vital sign modality are obtained to perform identity recognition. This method makes full use of the correlation and complementarity between different modal signals to improve the accuracy and reliability of identification. Simulation experiments show that the algorithm identity recognition proposed in this paper achieves an accuracy of 94.26% on a 20-subject self-test dataset, which is much higher than that of the traditional algorithm, which is about 85%.

Breathing temporarily pauses during swallowing, and the occurrence of inspiration before and after these pauses may increase the likelihood of aspiration, a serious health problem in older adults. Therefore, the automatic detection of these pauses without constraints is important. We propose methods for measuring respiratory movements during swallowing using millimeter wave radar to detect these pauses. The experiment involved 20 healthy adult participants. The results showed a correlation of 0.71 with the measurement data obtained from a band-type sensor used as a reference, demonstrating the potential to measure chest movements associated with respiration using a non-contact method. Additionally, temporary respiratory pauses caused by swallowing were confirmed by the measured data. Furthermore, using machine learning, the presence of respiring alone was detected with an accuracy of 88.5%, which is higher than that reported in previous studies. Respiring and temporary respiratory pauses caused by swallowing were also detected, with a macro-averaged F1 score of 66.4%. Although there is room for improvement in temporary pause detection, this study demonstrates the potential for measuring respiratory movements during swallowing using millimeter wave radar and a machine learning method.

Driving while drowsy poses significant risks, including reduced cognitive function and the potential for accidents, which can lead to severe consequences such as trauma, economic losses, injuries, or death. The use of artificial intelligence can enable effective detection of driver drowsiness, helping to prevent accidents and enhance driver performance. This research aims to address the crucial need for real-time and accurate drowsiness detection to mitigate the impact of fatigue-related accidents. Leveraging ultra-wideband radar data collected over five minutes, the dataset was segmented into one-minute chunks and transformed into grayscale images. Spatial features are retrieved from the images using a two-dimensional Convolutional Neural Network. Following that, these features were used to train and test multiple machine learning classifiers. The ensemble classifier RF-XGB-SVM, which combines Random Forest, XGBoost, and Support Vector Machine using a hard voting criterion, performed admirably with an accuracy of 96.6%. Additionally, the proposed approach was validated with a robust k-fold score of 97% and a standard deviation of 0.018, demonstrating significant results. The dataset is augmented using Generative Adversarial Networks, resulting in improved accuracies for all models. Among them, the RF-XGB-SVM model outperformed the rest with an accuracy score of 99.58%.

Vital sign monitoring is dominated by precise but costly contact-based sensors. Contactless devices such as radars provide a promising alternative. In this article, the effects of lateral radar positions on breathing and heartbeat extraction are evaluated based on a sleep study. A lateral radar position is a radar placement from which multiple human body zones are mapped onto different radar range sections. These body zones can be used to extract breathing and heartbeat motions independently from one another via these different range sections. Radars were positioned above the bed as a conventional approach and on a bedside table as well as at the foot end of the bed as lateral positions. These positions were evaluated based on six nights of sleep collected from healthy volunteers with polysomnography (PSG) as a reference system. For breathing extraction, comparable results were observed for all three radar positions. For heartbeat extraction, a higher level of agreement between the radar foot end position and the PSG was found. An example of the distinction between thoracic and abdominal breathing using a lateral radar position is shown. Lateral radar positions could lead to a more detailed analysis of movements along the body, with the potential for diagnostic applications.

With the continuous advancement of autonomous driving and monitoring technologies, there is increasing attention on non-intrusive target monitoring and recognition. This paper proposes an ArcFace SE-attention model-agnostic meta-learning approach (AS-MAML) by integrating attention mechanisms into residual networks for pedestrian gait recognition using frequency-modulated continuous-wave (FMCW) millimeter-wave radar through meta-learning. We enhance the feature extraction capability of the base network using channel attention mechanisms and integrate the additive angular margin loss function (ArcFace loss) into the inner loop of MAML to constrain inner loop optimization and improve radar discrimination. Then, this network is used to classify small-sample micro-Doppler images obtained from millimeter-wave radar as the data source for pose recognition. Experimental tests were conducted on pose estimation and image classification tasks. The results demonstrate significant detection and recognition performance, with an accuracy of 94.5%, accompanied by a 95% confidence interval. Additionally, on the open-source dataset DIAT- μ RadHAR, which is specially processed to increase classification difficulty, the network achieves a classification accuracy of 85.9%.

This paper considers a terahertz (THz) dual-function multi-input multi-output (MIMO) radar and communication system with the assistance of a reconfigurable intelligent surface (RIS) and jointly designs the constant modulus (CM) waveform and RIS phase shifts. A weighted optimization scheme is presented, to minimize the weighted sum of three objectives, including communication multi-user interference (MUI) energy, the negative of multi-target illumination power and the MIMO radar waveform similarity error under a CM constraint. For the formulated non-convex problem, a novel alternating coordinate descent (ACD) algorithm is introduced, to transform it into two subproblems for waveform and phase shift design. Unlike the existing optimization algorithms that solve each subproblem by iteratively approximating the optimal solution with iteration stepsize selection, the ACD algorithm can alternately solve each subproblem by dividing it into multiple simpler problems, to achieve closed-form solutions. Our numerical simulations demonstrate the superiorities of the ACD algorithm over the existing methods. In addition, the impacts of the weighting coefficients, RIS and channel conditions on the radar communication performance of the THz system are analyzed. [ABSTRACT FROM AUTHOR]

To address the recent increase in wildfire severity and incidence, as well as the subsequent financial and physical costs, forest managers and wildland firefighting agencies rely on remotely sensed products for better decision-making and mitigation efforts. To address the remote sensing needs of these agencies, which include high spatial resolution, immunity to atmospheric and solar illumination effects, and day/night capabilities, the use of synthetic aperture radar (SAR) is under investigation for application in current and upcoming systems for all phases of a wildfire. Focusing on the active phase, a method for monitoring wildfire activity is presented based on changes in the radar vegetation index (RVI). L-band backscatter measurements from NASA/JPL's UAVSAR instrument are used to obtain RVI images on multiple dates during the 2020 Bobcat (located in Southern CA, USA) and Hennessey (located in Northern CA, USA) fires and the 2021 Caldor (located in the Sierra Nevada region of CA, USA) fire. Changes in the RVI between measurement dates of a single fire are then compared to indicators of fire activity such as ancillary GIS-based burn extent perimeters and the Landsat 8-based difference normalized burn ratio (dNBR). An RVI-based wildfire "burn" detector/index is then developed by thresholding the RVI change. A combination of the receiver operating characteristic (ROC) curves and F1 scores for this detector are used to derive change detection thresholds at varying spatial resolutions. Six repeat-track UAVSAR lines over the 2020 fires are used to determine appropriate threshold values, and the performance is subsequently investigated for the 2021 Caldor fire. The results show good performance for the Bobcat and Hennessey fires at 100 m resolution, with optimum probability of detections of 67.89% and 71.98%, F1 scores of 0.6865 and 0.7309, and Matthews correlation coefficients of 0.5863 and 0.6207, respectively, with an overall increase in performance for all metrics as spatial resolution becomes coarser. The results for pixels identified as "burned" compare well with other fire indicators such as soil burn severity, known progression maps, and post-fire agency publications. Good performance is also observed for the Caldor fire where the percentage of pixels identified as burned within the known fire perimeters ranges from 37.87% at ~5 m resolution to 88.02% at 500 m resolution, with a general increase in performance as spatial resolution increases. All detections for Caldor show dense collections of burned pixels within the known perimeters, while pixels identified as burned that lie outside of the know perimeters have a sparse spatial distribution similar to noise that decreases as spatial resolution is degraded. The Caldor results also align well with other fire indicators such as soil burn severity and vegetation disturbance. [ABSTRACT FROM AUTHOR]

Bistatic radar exhibits spatial isomerism and diverse configurations, leading to unique clutter characteristics distinct from those of monostatic radar. The clutter rank serves as a pivotal indicator of clutter characteristics, enabling the quantification of clutter severity. Space-time adaptive processing (STAP) is a critical technique to detect moving targets, and clutter rank determines the number of independent and identically distributed (IID) training samples and the degree of freedom (DOF) for effective suppression of clutter that STAP requires. Therefore, the accurate estimation of clutter rank for bistatic radar can provide a crucial indicator for designing and constructing STAP processors, thereby facilitating fast and efficient clutter suppression in bistatic radar systems. This study is based on the idea that clutter rank is the number of prolate spheroidal wave function (PSWF) orthogonal bases utilized for approximating the clutter signal. Firstly, the challenge of utilizing PSWF orthogonal bases for approximating the clutter signal in bistatic radar is elucidated. This pertains to the fact that, unlike monostatic radar clutter, bistatic radar clutter is not capable of being expressed as a single-frequency signal. The clutter rank estimation for bistatic radar is thus derived as the frequency bandwidth estimation. Secondly, to achieve this estimation, the frequency distribution of each individual scattering unit is investigated, thereby determining their extending frequency broadening (EFB) as compared to that of single-frequency. Subsequently, the integral average of EFB across the entire range bin is computed, ultimately enabling the acquisition of bistatic radar's frequency bandwidth. Finally, the estimation method is extended to non-side-looking mode and limited observation areas with pattern modulation. Simulation experiments confirm that our proposed method provides accurate clutter rank estimations, surpassing 99% proportions of large eigenvalues across various bistatic configurations, observation modes, and areas. [ABSTRACT FROM AUTHOR]

The measurement of the target azimuth angle using forward-looking radar (FLR) is widely applied in unmanned systems, such as obstacle avoidance and tracking applications. This paper proposes a semi-supervised support vector regression (SVR) method to solve the problem of small sample learning of the target angle with FLR. This method utilizes function approximation to solve the problem of estimating the target angle. First, SVR is used to construct the function mapping relationship between the echo and the target angle in beamspace. Next, by adding manifold constraints to the loss function, supervised learning is extended to semi-supervised learning, aiming to improve the small sample adaptation ability. This framework supports updating the angle estimating function with continuously increasing unlabeled samples during the FLR scanning process. The numerical simulation results show that the new technology has better performance than model-based methods and fully supervised methods, especially under limited conditions such as signal-to-noise ratio and number of training samples. [ABSTRACT FROM AUTHOR]

Holographic synthetic aperture radar (HoloSAR) introduces a cutting-edge three-dimensional (3-D) imaging mode to the field of synthetic aperture radar (SAR), enriching the scattering information of targets by observing them across multiple spatial dimensions. However, independent phase errors among baselines, such as those caused by platform jitter and measurement inaccuracies, pose significant challenges to imaging quality. The phase gradient autofocus (PGA) method effectively estimates phase errors, but struggles to accurately estimate the linear component, causing vertical shift in HoloSAR subaperture imaging result. Therefore, this paper proposes a PGA-based phase error compensation method for HoloSAR to address the vertical shift issue caused by linear phase errors. This method can achieve phase error correction in both the echo domain and image domain with enhanced efficiency. Experimental results of simulated targets and real data from the GOTCHA system demonstrate the effectiveness and practicality of the proposed method. [ABSTRACT FROM AUTHOR]

Airborne aircrafts are dependent on the Global Navigation Satellite System (GNSS), which is susceptible to interference due to the satellite base-station and cooperative communication. Synthetic aperture radar altimeter (SARAL) provides the ability to measure the topographic terrain for matching with Digital Elevation Model (DEM) to achieve positioning without relying on GNSS. However, due to the near-vertical coupling in the delay-Doppler map (DDM), the similarity of DDMs of adjacent apertures is high, and the probability of successful matching is low. To this end, a novel neural network of terrain matching and aircraft positioning is proposed based on the airborne SARAL imagery. The model-driven terrain matching and aircraft positioning network (TMP-Net) is capable of realizing aircraft positioning by utilizing the real-time DDMs to match with the DEM-based DDM references, which are generated by a point-by-point coupling mechanism between the airborne routine and ground terrain DEM. Specifically, the training dataset is established by a numerical simulation method based on a semi-analytical model. Therefore, DEM-based DDM references can be generated by forward deduction when only regional DEM can be obtained. In addition to the model-based DDM generation, feature extraction, and similarity measurement, an aircraft positioning module is added. Three different positioning methods are designed to achieve the aircraft positioning, where three-point weighting exhibits the best performance in terms of positioning accuracy. Due to the fact that both the weighted triplet loss and softmax loss are employed in a cooperative manner, the matching accuracy can be improved and the positioning error can be reduced. Finally, both simulated and measured airborne datasets are used to validate the effectiveness of the proposed algorithm. Quantitative and qualitative evaluations show the superiority. [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]

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]

The Bio-Radar is herein presented as a non-contact radar system able to capture vital signs remotely without requiring any physical contact with the subject. In this work, the ability to use the proposed system for emotion recognition is verified by comparing its performance on identifying fear, happiness and a neutral condition, with certified measuring equipment. For this purpose, machine learning algorithms were applied to the respiratory and cardiac signals captured simultaneously by the radar and the referenced contact-based system. Following a multiclass identification strategy, one could conclude that both systems present a comparable performance, where the radar might even outperform under specific conditions. Emotion recognition is possible using a radar system, with an accuracy equal to 99.7% and an F1-score of 99.9%. Thus, we demonstrated that it is perfectly possible to use the Bio-Radar system for this purpose, which is able to be operated remotely, avoiding the subject awareness of being monitored and thus providing more authentic reactions.

Contrasting the X-band phased array radar (XPAR) with the conventional S-Band dual-polarization mechanical scanning radar (SMSR), the XPAR offers superior temporal and spatial resolution, enabling a more refined depiction of the internal dynamics within convective systems. While both SMSR and XPAR data are extensively used in monitoring and alerting for severe convective weather, their comparative application in numerical weather prediction through data assimilation remains a relatively unexplored area. This study harnesses the Weather Research and Forecasting Model (WRF) and its data assimilation system (WRFDA) to integrate radial velocity and reflectivity from the Guangzhou SMSR and nine XPARs across Guangdong Province. Utilizing a three-dimensional variational approach at a 1 km convective-scale grid, the assimilated data are applied to forecast a rainstorm event in the Pearl River Delta (PRD) on 6 June 2022. Through a comparative analysis of the results from assimilating SMSR and XPAR data, it was observed that the assimilation of SMSR data led to more extensive adjustments in the lower- and middle-level wind fields compared to XPAR data assimilation. This resulted in an enlarged convergence area at lower levels, prompting an overdevelopment of convective systems and an excessive concentration of internal hydrometeor particles, which in turn led to spurious precipitation forecasts. However, the sequential assimilation of both SMSR and XPAR data effectively reduced the excessive adjustments in the wind fields that were evident when only SMSR data were used. This approach diminished the generation of false echoes and enhanced the precision of quantitative precipitation forecasts. Additionally, the lower spectral width of XPAR data indicates its superior detection accuracy. Assimilating XPAR data alone yields more reasonable adjustments to the low- to middle-level wind fields, leading to the formation of small-to-medium-scale horizontal convergence lines in the lower levels of the analysis field. This enhancement significantly improves the model's forecasts of composite reflectivity and radar echoes, aligning them more closely with actual observations. Consequently, the Threat Score (TS) and Equitable Threat Score (ETS) for heavy-rain forecasts (>10 mm/h) over the next 5 h are markedly enhanced. This study underscores the necessity of incorporating XPAR data assimilation in numerical weather prediction practices and lays the groundwork for the future joint assimilation of SMSR and XPAR data. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) and radar technology have benefitted from breakthroughs in recent decades. Both technologies have found applications independently of each other, but together, they also unlock new possibilities, especially for remote sensing applications. One of the key factors for a remote sensing system is the estimation of the flight attitude. Despite the advancements, accurate attitude estimation remains a significant challenge, particularly due to the limitations of a conventional Inertial Measurement Unit (IMU). Because these sensors may suffer from issues such as drifting, additional effort is required to obtain a stable attitude. Against that background, this study introduces a novel methodology for making an attitude estimation using radar data. Herein, we present a drone measurement system and detail its calculation process. We also demonstrate our results using three flight scenarios and outline the limitations of the approach. The results show that the roll and pitch angles can be calculated using the radar data, and we conclude that the findings of this research will help to improve the flight attitude estimation of remote sensing flights with a radar sensor. [ABSTRACT FROM AUTHOR]

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]

Due to the scheme of fixed-platform beam-steering radar and the space of the blast furnace being subjected to harsh environmental influences, the traditional detection methods of burden surface are limited by geometric distortion, noncoherent clutter, and noise interference, which leads to an increase in the image entropy value and the equivalent number of views, makes the density distribution of burden surface show a diffuse state, and greatly affects the stability and accuracy. In this paper, a new fixed-platform beam-steering radar synthetic aperture radar imaging method (FPBS-SAR) is proposed in the sensory domain of the blast furnace environment. From the perspective of fixed-platform beam-steering radar motion characteristics, the target range–azimuth coupled distance history model under the sub-aperture is established, the azimuthal Doppler variation characteristics of the fixed-platform beam-steering process are analyzed, and the compensation function of the transform domain for geometric disturbance correction is proposed. For noncoherent noise suppression in blast furnaces, the trimmed geometric mean-order-likelihood CFAR method is proposed to take into account the information of burden surface and clutter suppression. To verify the method, point target simulation and imaging for the industrial field measurement data are carried out. The results indicate that geometric distortion is well eliminated, the image entropy value and the equivalent number of views have decreased, and noncoherent noise in blast furnaces is suppressed. [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.

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 (CWT) 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.

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.

In this study, wind speed sensitivity and calibration bias were first determined for Surface Water and Ocean Topography (SWOT) satellite Ka-band Radar Interferometer (KaRIn) Normalized Radar Backscatter Cross Section (NRCS) data at VV and HH polarizations. Here, the calibration bias was estimated by comparing the KaRIn NRCS with collocated simulations from a model developed using Global Precipitation Measurement (GPM) satellite Dual-frequency Precipitation Radar (DPR) data. To recalibrate the bias, the correlation coefficient between the KaRIn data and the simulations was estimated, and the data with the corresponding top 10% correlation coefficients were used to estimate the recalibration coefficients. After recalibration, a Ka-band NRCS model was developed from the KaRIn data to retrieve ocean surface wind speeds. Finally, wind speed retrievals were evaluated using the collocated European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis winds, Haiyang-2C scatterometer (HY2C-SCAT) winds and National Data Buoy Center (NDBC) and Tropical Atmosphere Ocean (TAO) buoy winds. Evaluation results show that the Root Mean Square Error (RMSE) at both polarizations is less than 1.52 m/s, 1.34 m/s and 1.57 m/s, respectively, when compared to ECMWF, HY2C-SCAT and buoy collocated winds. Moreover, both the bias and RMSE were constant with the incidence angles and polarizations. This indicates that the winds from the SWOT KaRIn data are capable of correcting the sea state bias for sea surface height products. [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]

Weather radar is an active remote sensing device used to monitor the full lifecycle changes in severe convective weather with high spatial and temporal resolution. Effective radar calibration is a crucial foundation for ensuring the high-quality application of observational data. This paper utilizes a UAV platform equipped with a high-precision RTK system and standard metal spheres to study the principles and methods of metal sphere calibration, constructing a complete calibration process and calibration accuracy evaluation metrics. Additionally, a collocated radar comparison observation experiment was conducted for cross-validation, and metal sphere calibration tests were performed on problematic radars. The experimental results indicate the following: (1) The combined application of a high-precision RTK system and a laser range camera can provide real-time position information on the metal sphere, improving the efficiency of radar target acquisition. (2) The calibration method based on UAV-suspended metal spheres can periodically conduct the quantitative calibration of Z and ZDR, achieving calibration accuracies within 0.5 dB and 0.2 dB, respectively, and supports the qualitative inspection of key parameters such as beamwidth and pulse width. (3) During field tests, a high success rate "coarse adjustment + fine adjustment + staring" sphere-finding technique was established, based on automatic switching between RHI, PPI, and FIX scanning modes. This method directs the UAV to adjust the metal sphere to the center of the radar distance bin, reducing the impact of uneven beam filling and bin crossing, ensuring the accuracy of scattering characteristic measurements. [ABSTRACT FROM AUTHOR]

This paper presents exploratory work on the use of Testing with Concept Activation Vectors (TCAV) within a concept-based explanation framework to provide the explainability of millimeter-wave (MMW) radar target recognition. Given that the radar spectrum is difficult for non-domain experts to understand visually, defining concepts for radar remains a significant challenge. In response, drawing from the visual analytical experience of experts, some basic concepts based on brightness, striping, size, and shape are adopted in this paper. However, the simplicity of basic concept definitions sometimes leads to vague correlations with recognition targets and significant variability among individuals, limiting their adaptability to specific tasks. To address these issues, this study proposes a Basic Concept-Guided Deep Embedding Clustering (BCG-DEC) method that can effectively discover task-specific composite concepts. BCG-DEC methodically analyzes the deep semantic information of radar data through four distinct stages from the perspective of concept discovery, ensuring that the concepts discovered accurately conform to the task-specific property of MMW radar target recognition. The experimental results show that the proposed method not only expands the number of concepts but also effectively solves the problem of difficulty in annotating basic concepts. In the ROD2021 MMW radar explainability experiments, the concepts proved crucial for recognizing specific categories of radar targets. [ABSTRACT FROM AUTHOR]

Autonomous driving technology is considered the trend of future transportation. Millimeter-wave radar, with its ability for long-distance detection and all-weather operation, is a key sensor for autonomous driving. The development of various technologies in autonomous driving relies on extensive simulation testing, wherein simulating the output of real radar through radar models plays a crucial role. Currently, there are numerous distinctive radar modeling methods. To facilitate the better application and development of radar modeling methods, this study first analyzes the mechanism of radar detection and the interference factors it faces, to clarify the content of modeling and the key factors influencing modeling quality. Then, based on the actual application requirements, key indicators for measuring radar model performance are proposed. Furthermore, a comprehensive introduction is provided to various radar modeling techniques, along with the principles and relevant research progress. The advantages and disadvantages of these modeling methods are evaluated to determine their characteristics. Lastly, considering the development trends of autonomous driving technology, the future direction of radar modeling techniques is analyzed. Through the above content, this paper provides useful references and assistance for the development and application of radar modeling methods. [ABSTRACT FROM AUTHOR]

The orthogonal frequency-division multiplexing (OFDM) mode with a linear frequency modulation (LFM) signal as the baseband waveform has been widely studied and applied in multiple-input multiple-output (MIMO) radar systems. However, its high sidelobe levels after pulse compression affect the target detection of radar systems. For this paper, theoretical analysis was performed, to investigate the causes of high sidelobe levels in OFDM-LFM waveforms, and a novel waveform optimization design method based on deep neural networks is proposed. This method utilizes the classic ResNeXt network to construct dual-channel neural networks, and a new loss function is employed to design the phase and bandwidth of the OFDM-LFM waveforms. Meanwhile, the optimization factor is exploited, to address the optimization problem of the peak sidelobe levels (PSLs) and integral sidelobe levels (ISLs). Our numerical results verified the correctness of the theoretical analysis and the effectiveness of the proposed method. The designed OFDM-LFM waveforms exhibited outstanding performance in pulse compression and improved the detection performance of the radar. [ABSTRACT FROM AUTHOR]

In autonomous driving, Frequency-Modulated Continuous-Wave (FMCW) radar has gained widespread acceptance for target detection due to its resilience and dependability under diverse weather and illumination circumstances. Although deep learning radar target identification models have seen fast improvement, there is a lack of research on their susceptibility to adversarial attacks. Various spoofing attack techniques have been suggested to target radar sensors by deliberately sending certain signals through specialized devices. In this paper, we proposed a new adversarial deep learning network for spoofing attacks in radar target detection (RTD). Multi-level adversarial attack prevention using deep learning is designed for the coherence pulse deep feature map from DAALnet and Range-Doppler (RD) map from TDDLnet. After the discrimination of the attack, optimization of hybrid deep learning (OHDL) integrated with enhanced PSO is used to predict the range and velocity of the target. Simulations are performed to evaluate the sensitivity of AOHDL for different radar environment configurations. RMSE of AOHDL is almost the same as OHDL without attack conditions and it outperforms the earlier RTD implementations. [ABSTRACT FROM AUTHOR]

A graphene-based tunable polarization conversion metasurface (PCM) was designed and analyzed for the purpose of reducing the radar cross-section (RCS) of array antennas. The metasurface comprises periodic shuttle-shaped metal patches, square-patterned graphene, and inclined grating-patterned graphene. By adjusting the Fermi energy levels of the upper (μ1) and lower (μ2) graphene layers, different states were achieved. In State 1, with μ1 = 0 eV and μ2 = 0.5 eV, the polarization conversion ratio (PCR) exceeded 0.9 in the bandwidths of 1.65–2.19 THz and 2.29–2.45 THz. In State 2, with μ1 = μ2 = 0.5 eV, the PCR was greater than 0.9 in the 1.23–1.85 THz and 2.24–2.60 THz bands. In State 3, with μ1 = μ2 = 1 eV, the PCR exceeded 0.9 in the 2.56–2.75 THz and 3.73–4.05 THz bands. By integrating the PCM with the array antenna, tunable RCS reduction was obtained without affecting the basic radiation functionality of the antenna. In State 1, RCS reduction was greater than 10 dB in the 1.60–2.43 THz and 3.63–3.72 THz frequency ranges. In State 2, the RCS reduction exceeded 10 dB in the 2.07–2.53 THz, 2.78–2.98 THz, and 3.70–3.81 THz bands. In State 3, RCS reduction was greater than 10 dB in the 1.32–1.43 THz, 2.51–2.76 THz, and 3.76–4.13 THz frequency ranges. This polarization conversion metasurface shows significant potential for applications in switchable and tunable antenna RCS reduction. [ABSTRACT FROM AUTHOR]

There is an urgent need to develop non-destructive testing (NDT) methods for infrastructure facilities and residences, etc., where human lives are at stake, to prevent collapse due to aging or natural disasters such as earthquakes before they occur. In such inspections, it is desirable to develop a remote, non-contact, non-destructive inspection method that can inspect cracks as small as 0.1 mm on the surface of a structure and damage inside and on the surface of the structure that cannot be seen by the human eye with high sensitivity, while ensuring the safety of the engineers inspecting the structure. Based on this perspective, we developed a radar module (absolute gain of the transmitting antenna: 13.5 dB; absolute gain of the receiving antenna: 14.5 dB) with very high directivity and minimal loss in the signal transmission path between the radar chip and the array antenna, using our previously developed technology. A single-input, multiple-output (SIMO) synthetic aperture radar (SAR) imaging system was developed using this module. As a result of various performance evaluations using this system, we were able to demonstrate that this system has a performance that fully satisfies the abovementioned indices. First, the SNR in millimeter-wave (MM-wave) imaging was improved by 5.4 dB compared to the previously constructed imaging system using the IWR1443BOOST EVM, even though the measured distance was 2.66 times longer. As a specific example of the results of measurements on infrastructure facilities, the system successfully observed cracks as small as 0.1 mm in concrete materials hidden under glass fiber-reinforced tape and black acrylic paint. In this case, measurements were also made from a distance of about 3 m to meet the remote observation requirements, but the radar module with its high-directivity and high-gain antenna proved to be more sensitive in detecting crack structures than measurements made from a distance of 780 mm. In order to estimate the penetration length of MM waves into concrete, an experiment was conducted to measure the penetration of MM waves through a thin concrete slab with a thickness of 3.7 mm. As a result, Λexp = 6.0 mm was obtained as the attenuation distance of MM waves in the concrete slab used. In addition, transmission measurement experiments using a composite material consisting of ceramic tiles and fireproof board, which is a component of a house, and experiments using composite plywood, which is used as a general housing construction material in Japan, succeeded in making perspective observations of defects in the internal structure, etc., which are invisible to the human eye. [ABSTRACT FROM AUTHOR]

In this study, we address the challenge of accurately classifying human movements in complex environments using sensor data. We analyze both video and radar data to tackle this problem. From video sequences, we extract temporal characteristics using techniques such as motion history images (MHI) and Hu moments, which capture the dynamic aspects of movement. Radar data are processed through principal component analysis (PCA) to identify unique detection signatures. We refine these features using k-means clustering and employ them to train hidden Markov models (HMMs). These models are tailored to distinguish between distinct movements, specifically focusing on differentiating sitting from falling motions. Our experimental findings reveal that integrating video-derived and radar-derived features significantly improves the accuracy of motion classification. Specifically, the combined approach enhanced the precision of detecting sitting motions by over 10% compared to using single-modality data. This integrated method not only boosts classification accuracy but also extends the practical applicability of motion detection systems in diverse real-world scenarios, such as healthcare monitoring and emergency response systems. [ABSTRACT FROM AUTHOR]

Efficient and effective radar emitter recognition is critical for electronic support measurement (ESM) systems. However, in complex electromagnetic environments, intercepted pulse trains generally contain substantial data noise, including spurious and missing pulses. Currently, radar emitter recognition methods utilizing traditional artificial neural networks (ANNs) like CNNs and RNNs are susceptible to data noise and require intensive computations, posing challenges to meeting the performance demands of modern ESM systems. Spiking neural networks (SNNs) exhibit stronger representational capabilities compared to traditional ANNs due to the temporal dynamics of spiking neurons and richer information encoded in precise spike timing. Furthermore, SNNs achieve higher computational efficiency by performing event-driven sparse addition calculations. In this paper, a lightweight spiking neural network is proposed by combining direct coding, leaky integrate-and-fire (LIF) neurons, and surrogate gradients to recognize radar emitters. Additionally, an improved SNN for radar emitter recognition is proposed, leveraging the local timing structure of pulses to enhance adaptability to data noise. Simulation results demonstrate the superior performance of the proposed method over existing methods. [ABSTRACT FROM AUTHOR]

Under rainy conditions, atmospheric settling particles have significant depolarization effects on radar waves and thus affect the target detection performance. This paper establishes an extended target detection model for fully polarized radar under complex rainy conditions and proposes two polarization and waveform optimization methods by taking into account the transmission effect of rainfall. If the rainfall information is known, the polarization and waveform for transmitting and receiving are optimized by maximizing the system Signal-to-Clutter/Noise Ratio (SCNR). While the rainfall information is absent, the optimal transmitting polarization and waveform are obtained by directly compensating the transmission effect from non-rainfall optimization results using fully polarized radar observation parameters. Using measured data from the T-72 tank, The experimental results verify the effectiveness and robustness of the two methods in real scenarios and 4 dB on the improvement of SCNR can be achieved. [ABSTRACT FROM AUTHOR]

A dual-polarized compact Vivaldi antenna with high gain performance is proposed for tree radar applications. The proposed design introduces an array configuration consisting of two pairs of two Vivaldi elements to optimize the operating bandwidth and gain while providing dual-polarization capability. To enhance the gain of the proposed antenna over a certain frequency range of interest, directors and edge slots are incorporated into each Vivaldi element. To further enhance the overall antenna gain, a metal back reflector is used. The measurement results of the fabricated antenna show that the proposed antenna achieves a high gain of 5.5 to 14.8 dBi over broadband from 0.5 GHz to 3 GHz. Moreover, it achieves cross-polarization discrimination larger than 20 dB, ensuring high polarization purity. The fabricated antenna is used to detect and image the defects inside tree trunks. The results show that the proposed antenna yields a better-migrated image with a clear defect region compared to that obtained by a commercial Horn antenna. [ABSTRACT FROM AUTHOR]

In the increasingly complex electromagnetic environment, main lobe jamming significantly degrades the performance of a wideband radar system. To mitigate this issue, this paper developed a wideband main lobe jamming suppression algorithm based on a distributed array radar. Firstly, this algorithm utilizes eigen-projection matrix processing in the main array to cancel out the main lobe jamming for main lobe maintenance, and then suppresses side lobe jamming through null constraint beamforming. Subsequently, the large aperture of the full array is leveraged to form a narrow beam directed toward the main lobe interference. Finally, joint beamforming using the minimum mean square error criterion is employed. In scenarios where both main lobe jamming and side lobe jamming exist, this algorithm can adaptively cancel main lobe wideband jamming, suppress side lobe wideband jamming, and effectively control the significant loss of the desired wideband signals caused by main lobe jamming within a smaller angular range. Simulation results validate the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Nowadays, asteroid and comet exploration is one of the most important components of deep space exploration. Through asteroid and comet exploration missions, it is possible to reveal the history of the formation and evolution of the solar system, to understand the origin and evolution of the planets, and to improve scientific models and instruments. As a payload with the advantages of non-destructive, penetrating, and polarizing characteristics, ground-penetrating radar (GPR) has been widely used in lunar and Mars exploration, and will play an important role in planned asteroid and comet exploration missions. In this study, statistics on asteroid and comet exploration missions, scientific results, and space-based ground-penetrating radar (SB-GPR) utilization are presented for the three phases to date. According to the statistics, SB-GPR will play an important role in future Phase 2 and 3 missions. The focus of this study is on analyzing the mission flow, SB-GPR parameters, scientific objectives, and scientific results of the missions that have carried SB-GPR and those that are planned to carry SB-GPR, including the Hera, Rosetta, Castalia, and Tianwen-2 missions. On this basis, the development trends of asteroid and comet exploration missions, as well as the future development trends of SB-GPR design and signal interpretation, are discussed. [ABSTRACT FROM AUTHOR]

Abstract: While software-defined radars can switch their transmitted waveform on the go, they cannot transmit all waveforms at the same time, meaning they must balance the advantages and drawbacks of each configuration. Here, we propose theoretically and demonstrate experimentally the universal radar, which can apply the desired waveform in the post-processing stage after the physical measurement has been performed. This method also allows using a single measurement of a scene to design and test any other radar in complex scenarios without having to take it to the field. The method is based on post-processing the frequency response measured by a synthetically broadband stepped-frequency continuous wave radar, such as a vector network analyzer. An algorithm for overcoming distortions due to moving targets is derived as well. This approach not only provides an ultra-wideband software-only defined radar, but it also enables the acquired data from any measured site to be used for the design and analysis of almost any other future radar system, significantly cutting the time and cost of new developments. The method suggests the creation of radar raw data repositories that can be shared across diversely different radar platforms. [ABSTRACT FROM AUTHOR]

Battlefield surveillance radar is usually 2-D radar, which cannot realize target three-dimensional localization, leading to poor resolution for the air target in the elevation dimension. Previous researchers have used the Traditional Height Finder Radar (HFR) or multiple 2-D radar networking to estimate the target three-dimensional location. However, all of them face the problems of high cost, poor real-time performance and high requirement of space–time registration. In this paper, Reconfigurable Intelligent Surfaces (RISs) with low cost are introduced into the 2-D radar to realize the target three-dimensional localization. Taking advantage of the wide beam of 2-D radar in the elevation dimension, several Unmanned Aerial Vehicles (UAVs) carrying RISs are set in the receiving beam to form multiple auxiliary measurement channels. In addition, the traditional 2-D radar measurements combined with the auxiliary channel measurements are used to realize the target three-dimensional localization by solving a nonlinear least square problem with a convex optimization method. For the proposed RIS-assisted target three-dimensional localization problem, the Cramer–Rao Lower Bound (CRLB) is derived to measure the target localization accuracy. Simulation results verify the effectiveness of the proposed 3-D localization method, and the influences of the number, the positions and the site errors of the RISs on the localization accuracy are covered. [ABSTRACT FROM AUTHOR]