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Abstract In the design of horn array antennas, usually there is a challenge with the grating lobe problem and consequently quite low aperture efficiency. A new configuration for horn array antennas is presented which introduces high aperture efficiency with suppressed grating lobes in both E‐& H‐plane radiation patterns. The proposed array unit cell is a dual‐mode horn loaded by two corrugations in E‐plane. A sample 4 ⨯ 4 array of the proposed unit cell is designed to operate at Ku band. The simulation and measurement results show 13.3% operating bandwidth over which the grating lobes are totally suppressed and the aperture efficiency is above 70%. The antenna measured gain is also 27 dBi with 2 dB variation over the bandwidth. The results show that the aperture efficiency of 98% is achievable making use of the proposed topology.

Abstract A novel method based on machine learning is proposed to estimate the electromagnetic radiation level at the ground plane near fifth‐generation (5G) base stations. The machine learning model was trained using data from various 5G base stations, enabling it to estimate the electric field intensity at any arbitrary radiation point when the base station provides service to different numbers of 5G terminals which are in different service modes. The inputs required for the model include the transmit power of the antenna, the antenna gain, the distance between the 5G base station and 5G terminals, terminal service modes, the number of 5G terminals and the environmental complexity around the 5G base station. Experimental results demonstrate the feasibility and effectiveness of the estimation method, with the mean absolute percentage error of the machine learning model being approximately 5.98%. This level of accuracy showcases the reliability of the approach. Moreover, the proposed method offers low costs when compared with on‐site measurements. The estimated results can be utilised to reduce test costs and provide valuable guidance for optimal site selection, thereby facilitating radio wave coverage or electromagnetic radiation regulation of 5G base stations.

Abstract Conventionally, both electrically larger (EL) arrays and sparse arrays offer the advantage of element number reduction but disadvantage of high sidelobe levels. A new scheme of planar EL sparse array antenna based on a genetic algorithm (GA) to achieve low sidelobe with element number reduction is proposed. To begin with, EL sparse array antenna optimisation models based on GA for both linear and planar arrays are analysed. Then, an EL slot antenna element based on a 3 × 3 substrate integrated waveguide cavity is designed. An 8‐element linear EL sparse array antenna is designed and compared with a uniform array antenna, demonstrating a reduction in the maximum sidelobe level (MSLL) by nearly 4.6 dB. After that, a 4 × 8 element planar EL sparse array antenna is fabricated and measured. Compared to an 8 × 16 element planar EL uniform array antenna, the number of antenna elements is reduced by 75%, while the MSLL is reduced by approximately 3 dB. The measured −10 dB impedance bandwidth ranges from 25.3 to 27.8 GHz. At the central frequency, the radiation pattern achieves a peak gain of 29.6 dBi, exhibiting low sidelobe levels below −15.0 dB.

Abstract A wideband end‐fire stepped slot antenna with gain enhancement and filtering capability is proposed. On one hand, a parasitic dipole serving as a director is introduced in the stepped slot to improve the radiation gain of the slot antenna without increasing its physical size. It is found that a gain enhancement of about 2 dB can be achieved after adopting such approach. On the other hand, an absorptive branch possessing a nearly complementary frequency response with regard to the one of the slot antenna is in‐parallel loaded at the microstrip feedline. It allows the partial dissipation of the out‐of‐band/non‐radiated RF‐signal power of the antenna to attain both an RF‐quasi‐absorptive behaviour and filtering capability. It is found that after loading the dissipative branch, the out‐of‐band radiation efficiency of the slot antenna is reduced to less than 20%. The measured results of a fabricated proof‐of‐concept antenna prototype show a reasonable agreement with the simulated ones and verify the RF performance merits of the proposed stepped slot antenna, such as wide RF operating band covering the 3.39−4.73‐GHz range, end‐fire radiation with a front‐to‐back ratio (FBR) above 10 dB, flat radiation gain, and co‐integrated filtering functionality with RF‐quasi‐absorptive capability.

Abstract Traditional quarter‐wavelength parallel‐line couplers (QWPLC) in microstrip form suffer heavily in low directivity because of unbalanced even‐mode and odd‐mode phase velocities. Related schemes to improve directivity lead to complex structures and large dimensions. This study derives and validates an iterative synthesis method to design a high‐directivity microstrip coupler adopting a symmetric stepped‐impedance microstrip coupled line (SSIMCL). Given that the even‐mode and odd‐mode effective dielectric constants (EDCs) vary with the characteristic impedances, the authors take the EDCs of traditional QWPLC as the initial values for an SSIMCL, and the characteristic impedances and EDCs can be computed and converged in several iterations, resulting in good phase delay balance and high coupler directivity. A 20‐dB microstrip coupler working at 1 GHz with high directivity is designed, fabricated, and measured. The obtained maximum directivity is 47 dB and approximately 1 GHz, while the relative bandwidth with a directivity higher than 20 dB and a return loss better than 20 dB is larger than 70%. The novel microstrip coupler also features a compact, simple structure shorter than λg/4.

Abstract Continuous beam steering approach with minimal power consumption is highly desirable in modern antenna designs. A simple mechanical method for achieving beam steering in the Fabry–Perot resonator antenna (FPRA) is presented. It involves rotating the upper phase gradient metasurface (PGM) mechanically to change the aperture phase distribution, so the beam is continuously steered in the azimuthal plane while maintaining a large elevation angle. The proposed PGM unit cell comprises a hexagonal ring and patch printed on both sides of the substrate, along with a honeycomb lattice. A prototype antenna operating at 5.65 GHz is fabricated and measured to validate the feasibility. Measurement results show that the FPRA achieves a gain of 14.9 dBi, and can continuously steer its beam in the azimuthal plane with an elevation angle of around θ = 50°. Measured radiation patterns in eight azimuthal directions (φ = 0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°) are consistent with the simulated results. Compared with other electrical tuning methods, our design has a compact size and requires lower power for the PGM rotation.

Abstract The authors extend the resonant modal theory (RMT) developed previously for a metal object to an arbitrary source region consisting of metals, dielectrics, or a combination of both. The influences of dielectrics on the fields are replaced by equivalent volume sources through the use of the compensation theorem in electromagnetic theory. The resonant frequencies can be determined by finding the roots of the determinant of the matrix resulted from the discretisation of the real homogeneous volume–surface integral equation derived from the requirement that the difference of stored field energies in the source region vanishes. As applications of the extended RMT, three examples have been investigated. The first example is a dielectric resonator antenna and is designed by exciting the first resonant mode of the composite structure in which the dielectric cylinder is combined with a conformal metallic strip. The second example is a dual‐band dielectric‐coated metallic wire antenna. The third example studies the resonant modes of a rectangular patch antenna.

Abstract The electromagnetic compatibility analysis of RF circuitry is essential due to the adverse effects on sensitive components. Two metamaterial unit cells with slow‐wave properties are presented for cross‐talk reduction in transmission lines. The analytical expressions for computing equivalent lumped elements of the lines are then provided. The dispersion diagram is obtained from the parameters extracted from the measurement. A microstrip as the generator line and five identical CRLH unit cells as the receptor line are then used and a significant reduction in cross‐talk is observed in comparison to conventional lines. A non‐uniform transmission line with two different CRLH unit cells as the receptor line is then employed and found effective in further cross‐talk reduction between the lines. The lengths of the lines and numbers of cells are kept the same in all lines for comparison. The simulated results are validated by measurements.

Dynamic processes in molecules can occur on a wide range of timescales, and it is important to understand which timescales of motion contribute to different parameters used in dynamics measurements. For spin relaxation, this can easily be understood from the sampling frequencies of the spectral-density function by different relaxation-rate constants. In addition to data from relaxation measurements, determining dynamically averaged anisotropic interactions in magic-angle spinning (MAS) solid-state NMR allows for better quantification of the amplitude of molecular motion. For partially averaged anisotropic interactions, the relevant timescales of motion are not so clearly defined. Whether the averaging depends on the experimental methods (e.g., pulse sequences) or conditions (e.g., MAS frequency, magnitude of anisotropic interaction, radio-frequency field amplitudes) is not fully understood. To investigate these questions, we performed numerical simulations of dynamic systems based on the stochastic Liouville equation using several experiments for recoupling the dipolar coupling, chemical-shift anisotropy or quadrupolar coupling. As described in the literature, the transition between slow motion, where parameters characterizing the anisotropic interaction are not averaged, and fast motion, where the tensors are averaged leading to a scaled anisotropic quantity, occurs over a window of motional rate constants that depends mainly on the strength of the interaction. This transition region can span 2 orders of magnitude in exchange-rate constants (typically in the microsecond range) but depends only marginally on the employed recoupling scheme or sample spinning frequency. The transition region often coincides with a fast relaxation of coherences, making precise quantitative measurements difficult. Residual couplings in off-magic-angle experiments, however, average over longer timescales of motion. While in principle one may gain information on the timescales of motion from the transition area, extracting such information is hampered by low signal-to-noise ratio in experimental spectra due to fast relaxation that occurs in the same region.

Nuclear magnetic resonance (NMR) technology can directly detect hydrogen signals in hydrogen-containing fluids, such as water and hydrocarbon. It can provide quantitative characterization of the fluid content and surrounding environment, thus finding extensive application in oil and gas exploration field. However, when measured samples containing ultra-fast relaxation components, such as shale oil and gas, the reliability of measurement results tends to be very low because only a small number of front-end signals in Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence can be obtained for the fast relaxation components. To address this problem, a multiple micro echo interval measurement (MCTEM) method was proposed in this research. In the measurement stage, the proposed method acquired more NMR front-end signals. In the inversion stage, multiple echo data were adopted for the joint inversion. It contributed to forming an accurate fast relaxation T2 spectrum. MCTEM method was validated through numerical simulations and rock physics experiments. It was concluded from the results that MCTEM method could improve the problem of peak disappearance for fast relaxation components in T2 spectrum caused by long echo interval. MCTEM method could enhance the measurement accuracy of fast relaxation signals effectively.

Investigating the moisture migration during the wood drying process can help improve wood utilization. Single-sided nuclear magnetic resonance (NMR) technology facilitates such investigation with its advantage in conducting one-dimensional measurements along different directions of wood, allowing for the detection of moisture transfer at different depths along the axial and transverse directions during the wood drying process. This research focused on Pinus sylvestris var.mongolica wood, on which a glue sealing technique was employed to ensure that the moisture only transfers along the axial or transverse axis, and the apparent transverse relaxation time (T2app) was utilized to delve into the changes of moisture content at various depths during the drying process. The results showed that at the first 2 hours of the drying process, there was a little free water near the evaporation surface of Pinus sylvestris var.mongolica wood, followed by a scarcity of free water during the drying process, and a noticeable moisture content gradient was observed near the evaporation surface. When the moisture transferred along the axial direction, the farther the moisture was away from the evaporation surface, the more uniform the moisture distribution was. When the moisture transferred along the tangential direction, the farther the moisture was away from the evaporation surface, the more obvious the moisture difference in each layer was. By single-sided NMR technology, it is possible to ascertain the moisture content of wood at various depths, thereby offering a theoretical framework for revealing the migration mechanism of water within wood.

With their high reliability, small size and low power consumption, rubidium atomic clocks are widely used in the fields of navigation, communication, etc. In particular, rubidium atomic clocks for navigation satellites have developed excellent stability. However, their inherent frequency drift characteristics (about E-12 to E-13/day) will deteriorate their long-term performance and affect the autonomous punctual timing of satellites. In this paper, we fully analyzed the frequency data of high-performance rubidium atomic clocks, aiming to figure out the physical mechanism behind the frequency drift. A high-precision frequency drift compensation scheme is proposed and experimentally verified. The results show that the drift rate of high-performance rubidium clock can be maintained in the order of E-15/day within 60 days without external taming, and the day stability can reach the order of E-15 (Allan variance), which greatly improves the autonomous punctual timing ability of rubidium atomic clock．

Laplace NMR can provide information on diffusion coefficients or relaxation time, serving as a powerful technology for studying molecular structure, dynamics, and interactions in samples. Generally, the applicability of Laplace NMR is subject to the performance of signal processing and reconstruction algorithms associated with the inverse Laplace transform. In this paper, we first discuss the ill-posed nature of the spectrum reconstruction problem for Laplace NMR, then revisit the classic regularization-based reconstruction algorithms and introduce the state-of-the-art deep-learning-based methods. In conclusion, the advantages and disadvantages of these algorithms are summarized, and future improvements for Laplace NMR signal processing methods are prospected.

Magnetic resonance elastography (MRE) is a method to estimate biomechanical properties of soft tissues by recording shear wave propagation using MR imaging. The wave excitation is produced by an external actuator and the properties are inversely calculated based on the wave equation. Biomechanical properties of brain tissue, especially the viscoelastic properties, are closely related to the growth, aging, and disease of brain. This review first introduces the theoretical background of MRE, followed by the physical meaning of the viscoelastic parameters and wave equations used for inversion. Scanning protocols for MRE, along with a specific example focusing on brain MRE, are also described. The paper presents various clinical applications of brain MRE, with a specific emphasis on brain tumors and neurodegenerative diseases. The application of viscoelastic properties as biomarkers in fundamental scientific research, disease diagnosis, and prognosis is discussed. We further highlight the current trends in brain MRE research covering both technical and clinical aspects, providing a reference for future neuroscience research and clinical applications.

As the aging population increases, the prevalence of cardiovascular disease rises annually. In this context, the evaluation of cardiac function using medical imaging techniques plays a pivotal role in the diagnosis and treatment of cardiovascular disease. Cardiac segmentation is a prerequisite for assessing cardiac function and has been closely studied by clinicians and scientific researchers. This paper provides a comprehensive review of the literature from the past decade on cardiac segmentation, categorizing the studies into traditional segmentation approaches and deep learning methodologies. Emphasis is placed on the detailed discussion of segmentation methods based on active contours and atlas models; deep learning algorithms based on U-Net and full convolution neural network (FCN) are also extensively discussed. In particular, this paper elaborates various approaches to enhance deep learning networks and achieve accurate segmentation of specific cardiac regions. These approaches include incorporating local modules, optimizing loss functions, and enhancing network architectures. A comprehensive summary of the aforementioned methods is presented, considering three imaging modalities: cardiac magnetic resonance imaging, computed tomography, and ultrasonic cardiogram. Lastly, the article concludes by summarizing the current research status and discussing research directions for further exploration.

Highly homogeneous magnetic fields in magnetic resonance spectrometers are an important guarantee for improving the quality of spectra for chemical structure analysis and kinetic information. The active shimming process using a shimming power supply to drive a shimming coil to produce a compensating electromagnetic field is an essential way to improve the homogeneity of the magnetic field. The high accuracy and stability of the shimming power supply’s output are crucial indicators for improving the active shimming capability and maintaining the shimming results. The paper develops a high-precision, high-stability, low-ripple active shimming power supply system for magnetic resonance spectrometers, and designs a shimming current driver (constant current source) with output state monitoring based on negative feedback control, coupled with an MCU control platform and host computer system to complete the closed-loop transmission of control commands and data between hardware and software, to achieve digital constant current output control. Under R=2.84 Ω, L=25.5 µH shimming coil load, the full-scale output response time is less than 18.9 µs, the output ripple peak-to-peak control is 30 mV, the positive and negative current output symmetry is good, and the maximum output deviation is 4.8‰ for long time operation. This power supply was used in a nuclear magnetic resonance spectrometer system using 0.5 T-Halbach configuration magnets, and the magnetic field homogeneity was optimised from 24.48 ppm (10-6) to 2.72 ppm by driving a first-order shimming coil. This work contributes to the integration of compact nuclear magnetic resonance spectrometer systems and the applications related to active shimming.

In recent years, Halbach magnet has been extensively used in miniaturized NMR spectrometers. However, the inhomogeneity of the magnetic field of permanent magnets poses a challenge to passive shimming method. In this paper, we conducted a passive shimming study of Halbach permanent magnet array structure which is mechanically adjustable. We modeled the relationship between the radial position of the magnetic blocks and magnetic field homogeneity. Then, an optimization algorithm combining the Levenberg-Marquardt method and quasi-Newton method was utilized to optimize the magnetic field homogeneity by adjusting the radial positions of the magnetic blocks. With this approach, the homogeneity of a 1.03 T Halbach magnet was improved from 7 391×10-6 to 154.23×10-6 in a sphere with a radius of 2.5 mm. This work provides a flexible and convenient passive shimming method for compact Halbach magnet, which has the potential to be applied in NMR spectrometers and other instruments that require high magnetic field homogeneity.

Brain age has become an important analysis object in the diagnosis and mechanism research of neurodegenerative diseases. There is no consistent conclusion on whether major depression increases the brain age of patients, and few studies in this direction have been conducted in the Chinese population. In this paper, a REST-meta-MDD (resting-state functional magnetic resonance imaging dataset of major depressive disorder) dataset collected from 25 hospitals in China was used to construct a convolutional neural network model based on high-resolution T1-weighted three-dimensional magnetic resonance images of brain structures to predict the brain age of patients and calculate the difference from the actual age. The mean absolute error and correlation coefficients of the final results were 3.16 and 0.93, and the mean brain age of the patients with major depression increased by 3.94 years compared with the healthy group, further confirming that major depression accelerates brain aging, and the severity of the disease is related to the gender, age, and education of the patients. Compared with the traditional machine learning algorithms, the average absolute error of the results obtained by this model is smaller and the correlation coefficient is higher.

The pancreas has always been one of the most challenging parts in medical image segmentation due to its complex anatomical structure and complex surrounding environment. Aiming at the above problems, a deep learning segmentation model combining dual decoding and global attention upsampling module (DGANet) is proposed. The model consists of an encoder and two decoders, where the latter realizes the full utilization of different depth feature information. The model applies the global attention upsampling module and high-level rich semantic information to guide the low-level selection of more accurate feature information. The data set provided by Changhai Hospital was used for experiments. The results showed that the average Dice similarity coefficient was 86.28%, Intersection-over-Union (IoU) was 0.77, and Hausdorff distance (HD) was 7.7 mm. The data confirmed the clinical value of this model in segmenting pancreatic cystic tumors.

Considering the problem of radar target detection in the sea clutter environment, this paper proposes a deep learning-based marine target detector. The proposed detector increases the differences between the target and clutter by fusing multiple complementary features extracted from different data sources, thereby improving the detection performance for marine targets. Specifically, the detector uses two feature extraction branches to extract multiple levels of fast-time and range features from the range profiles and the range-Doppler (RD) spectrum, respectively. Subsequently, the local-global feature extraction structure is developed to extract the sequence relations from the slow time or Doppler dimension of the features. Furthermore, the feature fusion block is proposed based on adaptive convolution weight learning to efficiently fuse slow-fast time and RD features. Finally, the detection results are obtained through upsampling and nonlinear mapping to the fused multiple levels of features. Experiments on two public radar databases validated the detection performance of the proposed detector.

Forward-looking imaging of airborne scanning radar is widely used in situation awareness, autonomous navigation and terrain following. When the radar is influenced by unintentional temporally sporadic electromagnetic interference or abnormal equipment performance, the echo signal contains outliers. Existing super-resolution methods can suppress outliers and improve azimuth resolution, but the real-time computing problem is not considered. In this study, we propose an airborne scanning radar super-resolution method to achieve fast forward-looking imaging when echo data are abnormal. First, we propose using the Student-t distribution to model noise. Then, the expectation-maximization method is used to estimate the parameters. Inspired by the truncated singular value decomposition method, we introduce the truncated unitary matrix into the estimation formula of the target scattering coefficient. Finally, the size of inverse matrix is reduced and the computational complexity of parameter estimation is reduced through matrix transformation. The simulation results show that the proposed method can improve the azimuth resolution of forward-looking imaging in a shorter time, and suppress outliers in echo data.

Metasurfaces are two-dimensional artificial structures with numerous subwavelength elements arranged periodically or aperiodically. They have demonstrated their exceptional capabilities in electromagnetic wave polarization manipulation, opening new avenues for manipulating electromagnetic waves. Metasurfaces exhibiting electrically controlled reconfigurable polarization manipulation have garnered widespread research interest. These unique metasurfaces can dynamically adjust the polarization state of electromagnetic waves through real-time modification of their structure or material properties via electrical signals. This article provides a comprehensive overview of the development of metasurfaces exhibiting electrically controlled reconfigurable polarization manipulation and explores the technological advancements of metasurfaces with different transmission characteristics in the microwave region in detail. Furthermore, it delves into and anticipates the future development of this technology.

Distributed radar with moving platforms can enhance the survivability and detection performance of a system, however, it is difficult to equip these platforms with sufficient communication bandwidth to transmit high-precision observed data, posing a great challenge to the high-performance detection of a distributed radar system. Because low-bit quantization can effectively reduce the computation cost and resource consumption of distributed radar systems, in this paper, we investigate the high-performance detection of multiple moving targets using the distributed radar system on moving platforms by adopting the low-bit quantization strategy. First, according to system resources, multipulse observed data of each node may be quantized with a low-bit quantizer and the likelihood function relative to the quantizer and states of multiple targets are derived. Subsequently, based on the convexity of the likelihood function relative to the unknown reflection coefficients, a joint estimation algorithm is designed for the Doppler shifts and reflection coefficients. Then, a generalized likelihood ratio test based multi-target detector is designed for detecting multiple targets in the surveillance area with unknown states, and deriving the constant false alarm rate detection threshold. Finally, the optimal low-bit quantizer is designed by deriving the asymptotic detection performance of the system, which effectively improves the detection performance and ensures robustness. Simulation experiments are conducted to analyze the detection and estimation performance of the proposed algorithm, thereby demonstrating the effectiveness of the proposed algorithm for weak signals, and showing that the low-bit quantized data can achieve detection and estimation performance close to that of the high-precision (16-bit quantization) data while consuming a complementary 20% of the communication bandwidth. Besides, according to the simulated results, the two-bit quantization strategy may be a trade-off between the detection performance and resource consumption of the distributed radar system.

To address the challenges in tracking complex maneuvering extended targets, an effective maneuvering extended target tracking method was proposed for irregularly shaped star-convex using a Transformer network. Initially, the alpha-shape algorithm was used to model the variations in the star-convex shape. In addition, a recursive approach was proposed to estimate the irregular shape of an extended target by detailed derivation in the Bayesian filtering framework. This approach accurately estimated the shape of a static star convex extended target. Moreover, through the structural redesign of the target state transition matrix and the real-time estimation of the maneuvering extended target’s state transition matrix using a transformer network, the accurate tracking of complex maneuvering targets was achieved. Furthermore, the real-time tracking of star convex maneuvering extended targets was achieved by fusing the estimated shape contours with motion states. This study focused on constructing certain complex maneuvering extended target tracking scenarios to assess the performance of the proposed method and the comprehensive estimation capabilities of the algorithm considering both shapes and motion states using multiple performance indicators.

Inverse Synthetic Aperture Radar (ISAR) images of spacecraft are composed of discrete scatterers that exhibit weak texture, high dynamics, and discontinuity. These characteristics result in sparse point clouds obtained using traditional algorithms for the Three-Dimensional (3D) reconstruction of spacecraft ISAR images. Furthermore, using point clouds to comprehensively describe the complete shape of targets is difficult, which consequently hampers the accurate extraction of the structural and pose parameters of the target. To address this problem, considering that space targets usually have specific modular structures, this paper proposes a method for abstracting parametric structural primitives from space target ISAR images to represent their 3D structures. First, the energy accumulation algorithm is used to obtain the sparse point cloud of the target from ISAR images. Subsequently, the point cloud is fitted using parameterized primitives. Finally, primitives are projected onto the ISAR imaging plane and optimized by maximizing their similarity with the target image to obtain the optimal 3D representation of the target primitives. Compared with the traditional point cloud 3D reconstruction, this method can provide a more complete description of the three-dimensional structure of the target. Meanwhile, primitive parameters obtained using this method represent the attitude and structure of the target and can directly support subsequent tasks such as target recognition and analysis. Simulation experiments demonstrate that this method can effectively achieve the 3D abstraction of space targets based on ISAR sequential images.

The modern radar confrontation situation is complex and changeable, and inter-system combat has become a basic feature. The overall system performance affects the initiative on the battlefield and even the final victory or defeat. By optimizing the beam resources of radar and jammers in a system, the overall performance can be improved, and the effective low-intercept detection effect can be obtained in the spatial and temporal domains. However, joint optimization of cooperative beamforming in the spatial and temporal domains is a nonconvex problem with complex multiparameter coupling. In this paper, an optimization model is established for a multitasking dynamic scene in the spatial and temporal domains. Radar detection performance is the optimization goal, while the interference performance and energy limitation of jammers are the constraints. To solve the model, a joint design method of space-time cooperative beamforming based on iterative optimization was proposed; that is, iterative optimization of radar transmitting, receiving, and multiple jammers transmitting beamforming vectors was alternately optimized. To solve the Quadratically Constrained Quadratic Programs (QCQP) problem with indefinite matrices for multijammer collaborative optimization, this paper is based on the Feasible Point Pursuit Successive Convex Approximation (FPP-SCA) algorithm. In other words, on the basis of the SCA algorithm, algorithm feasibility is ensured through reasonable relaxation by introducing relaxation variables and a penalty term, which solves the difficulty of obtaining a feasible solution when a problem contains indefinite matrices. Simulation results show that under the constraint of certain jammer energy, the proposed method achieves the effect of multiple jammers interfering with each enemy platform in the spatial and temporal domains to cover our radar detection. This effect is achieved while ensuring high-performance radar detection of the target without interference. Compared with traditional algorithms, the collaborative interference based on the FPP-SCA algorithm exhibits a better performance in the dynamic scene.

In this study, a collaborative radar selection and transmit resource allocation strategy is proposed for multitarget tracking applications in multiple distributed phased array radar networks with imperfect detection performance. The closed-form expression for the Bayesian Cramér-Rao Lower Bound (BCRLB) with imperfect detection performance is obtained and adopted as the criterion function to characterize the precision of target state estimates. The key concept of the developed strategy is to collaboratively adjust the radar node selection, transmitted power, and effective bandwidth allocation of multiple distributed phased array radar networks to minimize the total transmit power consumption in an imperfect detection environment. This will be achieved under the constraints of the predetermined tracking accuracy requirements of multiple targets and several illumination resource budgets to improve its radio frequency stealth performance. The results revealed that the formulated problem is a mixed-integer programming, nonlinear, and nonconvex optimization model. By incorporating the barrier function approach and cyclic minimization technique, an efficient four-step-based solution methodology is proposed to solve the resulting optimization problem. The numerical simulation examples demonstrate that the proposed strategy can effectively reduce the total power consumption of multiple distributed phased array radar networks by at least 32.3% and improve its radio frequency stealth performance while meeting the given multitarget tracking accuracy requirements compared with other existing algorithms.

Fine terrain classification is one of the main applications of Synthetic Aperture Radar (SAR). In the multiband fully polarized SAR operating mode, obtaining information on different frequency bands of the target and polarization response characteristics of a target is possible, which can improve target classification accuracy. However, the existing datasets at home and abroad only have low-resolution fully polarized classification data for individual bands, limited regions, and small samples. Thus, a multidimensional SAR dataset from Hainan is used to construct a multiband fully polarized fine classification dataset with ample sample size, diverse land cover categories, and high classification reliability. This dataset will promote the development of multiband fully polarized SAR classification applications, supported by the high-resolution aerial observation system application calibration and verification project. This paper provides an overview of the composition of the dataset, and describes the information and dataset production methods for the first batch of published data (MPOLSAR-1.0). Furthermore, this study presents the preliminary classification experimental results based on the polarization feature classification and classical machine learning classification methods, providing support for the sharing and application of the dataset.

Passive radars based on FM radio signals have low detection probability, high false alarm rates and poor accuracy, presenting considerable challenges to target tracking in radar networks. Moreover, a high false alarm rate increases the computational burden and puts forward high requirements for the real-time performance of networking algorithms. In addition, low detection probability and poor azimuth accuracy result in a lack of redundant information, making measurement association and track initiation challenging. To address these issues, this paper proposes an FM-based passive radar network based on the concepts of elementary hypothesis points and elementary hypothesis track, as well as a track initiation algorithm. First, we construct possible low-dimensional association hypotheses and solve for their corresponding elementary hypothesis points. Subsequently, we associate elementary hypothesis points from different frames to form multiple possible elementary hypothesis tracks. Finally, by combining multi-frame radar network data for hypothesis track judgment, we confirm the elementary hypothesis tracks corresponding to the real targets, and eliminate the false elementary hypothesis tracks caused by incorrect associations. Result reveal that the proposed algorithm has lower computational complexity and faster track initiation speed than existing algorithms. Moreover, we verified the effectiveness of the proposed algorithm using simulation and experimental results.

Weak target signal processing is the cornerstone and prerequisite for radar to achieve excellent detection performance. In complex practical applications, due to strong clutter interference, weak target signals, unclear image features, and difficult effective feature extraction, weak target detection and recognition have always been challenging in the field of radar processing. Conventional model-based processing methods do not accurately match the actual working background and target characteristics, leading to weak universality. Recently, deep learning has made significant progress in the field of radar intelligent information processing. By building deep neural networks, deep learning algorithms can automatically learn feature representations from a large amount of radar data, improving the performance of target detection and recognition. This article systematically reviews and summarizes recent research progress in the intelligent processing of weak radar targets in terms of signal processing, image processing, feature extraction, target classification, and target recognition. This article discusses noise and clutter suppression, target signal enhancement, low- and high-resolution radar image and feature processing, feature extraction, and fusion. In response to the limited generalization ability, single feature expression, and insufficient interpretability of existing intelligent processing applications for weak targets, this article underscores future developments from the aspects of small sample object detection (based on transfer learning and reinforcement learning), multidimensional and multifeature fusion, network model interpretability, and joint knowledge- and data-driven processing.

Scanning radar angular super-resolution technology is based on the relationship between the target and antenna pattern, and a deconvolution method is used to obtain angular resolution capabilities beyond the real beam. Most current angular super-resolution methods are based on ideal distortion-free antenna patterns and do not consider pattern changes in the actual process due to the influence of factors such as radar radome, antenna measurement errors, and non-ideal platform motion. In practice, an antenna pattern often has unknown errors, which can result in reduced target resolution and even false target generation. To address this problem, this paper proposes an angular super-resolution imaging method for airborne radar with unknown antenna errors. First, based on the Total Least Square (TLS) criterion, this paper considers the effect of the pattern error matrix and derive the corresponding objective function. Second, this paper employs the iterative reweighted optimization method to solve the objective function by adopting an alternative iteration solution idea. Finally, an adaptive parameter update method is introduced for algorithm hyperparameter selection. The simulation and experimental results demonstrate that the proposed method can achieve super-resolution reconstruction even in the presence of unknown antenna errors, promoting the robustness of the super-resolution algorithm.

Detection of small, slow-moving targets, such as drones using Unmanned Aerial Vehicles (UAVs) poses considerable challenges to radar target detection and recognition technology. There is an urgent need to establish relevant datasets to support the development and application of techniques for detecting small, slow-moving targets. This paper presents a dataset for detecting low-speed and small-size targets using a multiband Frequency Modulated Continuous Wave (FMCW) radar. The dataset utilizes Ku-band and L-band FMCW radar to collect echo data from six UAV types and exhibits diverse temporal and frequency domain resolutions and measurement capabilities by modulating radar cycles and bandwidth, generating an LSS-FMCWR-1.0 dataset (Low Slow Small, LSS). To further enhance the capability for extracting micro-Doppler features from UAVs, this paper proposes a method for UAV micro-Doppler extraction and parameter estimation based on the local maximum synchroextracting transform. Based on the Short Time Fourier Transform (STFT), this method extracts values at the maximum energy point in the time-frequency domain to retain useful signals and refine the time-frequency energy representation. Validation and analysis using the LSS-FMCWR-1.0 dataset demonstrate that this approach reduces entropy on an average by 5.3 dB and decreases estimation errors in rotor blade length by 27.7% compared with traditional time-frequency methods. Moreover, the proposed method provides the foundation for subsequent target recognition efforts because it balances high time-frequency resolution and parameter estimation capabilities.

The field of Synthetic Aperture Radar Automatic Target Recognition (SAR-ATR) lacks effective black-box attack algorithms. Therefore, this research proposes a migration-based black-box attack algorithm by combining the idea of the Momentum Iterative Fast Gradient Sign Method (MI-FGSM). First, random speckle noise transformation is performed according to the characteristics of SAR images to alleviate model overfitting to the speckle noise and improve the generalization performance of the algorithm. Second, an AdaBelief-Nesterov optimizer is designed to rapidly find the optimal gradient descent direction, and the attack effectiveness of the algorithm is improved through a rapid convergence of the model gradient. Finally, a quasihyperbolic momentum operator is introduced to obtain a stable model gradient descent direction so that the gradient can avoid falling into a local optimum during the rapid convergence and to further enhance the success rate of black-box attacks on adversarial examples. Simulation experiments show that compared with existing adversarial attack algorithms, the proposed algorithm improves the ensemble model black-box attack success rate of mainstream SAR-ATR deep neural networks by 3%～55% and 6.0%～57.5% on the MSTAR and FUSAR-Ship datasets, respectively; the generated adversarial examples are highly concealable.

This paper investigates various array beam shapes for high-power electronic warfare applications using an actual broadband array antenna. For use in a limited mounting space, a compact printed log periodic dipole array (LPDA) operating over a wide bandwidth is designed. The LPDA is then extended to an 8 × 1 array, with active element patterns (AEPs) obtained through simulation and measurement. Beam synthesis is performed by the superposition of several array patterns, which involves adjusting the weight of each AEP. To derive a synthesized array beam shape that is similar to the guided mask, the Taylor window weighting method is applied to each array pattern. Finally, beam synthesis for the flat-top beam, cosecant-squared beam, and iso-flux beam is performed using Taylor window-weighted AEPs, and the results are compared with those of beam shapes using ideal isotropic patterns. The results demonstrate that various beam shapes can be achieved for high-power electronic warfare applications using the AEPs of an actual array antenna.

This article presents a compact, planar, and circularly polarized array antenna operating in the W-band (84.5–110 GHz), with all its prototypes fabricated using a low-cost, traditional, microwave printed circuit board composed of Rogers RT/duroid 5880 (ɛr = 2.2, tanδ = 0.009). The final design that was fabricated and measured was a 4 × 4 array antenna having an overall size of 9 mm × 20 mm × 0.254 mm that used series feeding to reduce its sidelobes. Measurements of the 4 × 4 patch antenna array showed approximately 6.9% 3-dB axial ratio bandwidth along with 15.2 dBi maximum right-hand circularly polarized (RHCP) antenna gain at 100 GHz. The array antenna yielded RHCP radiation characterized by a low profile, low cross-polarization levels (91%). Additionally, a two-port MIMO antenna system was investigated by considering side-by-side and front-to-front configurations, both of which achieved good isolation and considerable envelope correlation coefficient and diversity gain values. Therefore, the proposed series array and MIMO antennas can be reasonable candidates for 6G applications of the sub-THz band (100–110 GHz) in ultra-high-speed wireless and satellite communication systems.

This paper proposes a novel design for a frequency reconfigurable and bandwidth-enhanced antenna for use in biomedical telemetry applications. Data pertaining to a patient’s body parameters, such as blood pressure, pulse, and temperature, are gathered using sensors and then transmitted to a remote place for monitoring. The proposed antenna is connected to a wearable transmitter, which transfers the body parameter data to a centrally located nearby control unit. The antenna operates in the 5.8 GHz band in single-band mode and in the 4.27 GHz (C band) and 5.8 GHz industrial, scientific, and medical (ISM) bands in dual-band mode. The use of ethylene-vinyl acetate foam as a substrate makes the structure waterproof and ultraviolet resistant. The basic antenna structure equipped with proximity coupling offers a front-to-back ratio (FBR) of 17.62 dB and a bandwidth of 122 MHz. With an additional upper patch and resonant slots, bandwidth enhancement of 82.85% and 11.57% improvement in the FBR are achieved, respectively. Overall, a maximum FBR of 19.66 dB and gain of 5.0 dBi are attained over the resonant frequency. The specific absorption rate is found to be 0.145 W/kg for 10 gram of tissue.

In this paper, the performances of spaceborne bistatic synthetic aperture radar (SAR) systems are evaluated and compared with those of the conventional monostatic SAR system. Drawing on information on the altitude of satellites and the pulse repetition frequency of the system, the distance between the satellites carrying the transmitter and the receiver is first determined to then obtain the maximum observable width by overlapping the blind ranges. The performances of the bistatic SAR for two satellites at the same altitude moving parallel to each other at the same speed are evaluated, and the results are compared with those obtained from a monostatic SAR system located at left or right end of the baseline. The resolutions, noise equivalent sigma zero, and ambiguities of the bistatic SAR are evaluated based on the windows between blind incident angles of a timing diagram. Subsequently, the most appropriate window is determined by comparing the evaluated performances of the bistatic SAR.

This paper proposes a residual flux detection method based on the different polarities of response currents. When a positive–negative alternating DC voltage is loaded, the direction of the residual flux can be determined by the difference between the various polarities of the response current waveforms. As a result, residual flux value can be calculated using the empirical formula that relates residual flux to the responce current. In this formula, unknown parameters can be obtained using the field-circuit coupling method. Finally, this paper employs a closed iron core as an example to obtain the corresponding formula and then verifies its accuracy through experiments. The results show that the accuracy of the proposed method is less than 5%, which is higher than that of other existing methods. The method presented in this paper not only accurately detects the residual flux of a transformer but also requires no additional energy from the transformer.

Output power is an important performance metric related to static or quasi-static wireless power transfer (WPT). As an important component of WPT, the electrical parameters of a magnetic coupling mechanism (MCM) are directly related to its output characteristics. Notably, an optimal WPT design is largely based on MCMs using the same primary and secondary coils. However, in some scenarios involving limited space, the physical structure and electrical parameters of the coils cannot be kept completely consistent. Based on the influence of physical parameters on coupling performance and system characteristics, an optimal design method for MCMs using different primary and secondary coils to ensure maximum output power is proposed. The best receiving coil within a limited space that achieves maximum power at a specific location was selected. Experiments were conducted, with the corresponding results verifying the correctness of theoretical and simulation analyses, as well as the effectiveness of the proposed method.

To achieve simple and reliable conversion from constant current (CC) charging mode to constant voltage (CV) charging mode in wireless power transfer systems, this paper proposes a hybrid topology equipped with a reconfigurable rectifier. An AC switch is adapted to simultaneously realize the change from half-bridge rectification to full-bridge rectification and achieve the shift from a topology with capacitors in the series compensated primary and secondary sides to a topology with capacitors in the series compensated primary side and the inductor-capacitor-inductor (LCL) compensated secondary side. Notably, communication between the transmitter and receiver sides is not necessary in the proposed method. Furthermore, the zero phase angle characteristic is successfully maintained in both charging modes. The experimental results obtained using the 48 V/0.7 A experimental prototype, which was built to validate the relevant theoretical analysis, show that output current/voltage can be achieved in CC/CV charging mode independent of load resistance. Notably, the maximum power transfer efficiency of the system during the charging process reached 95.33%.

This article presents a transmit/receive switch that utilizes a broadband microstripline hybrid coupler. The proposed switch is suitable for application in both 3T and 7T magnetic resonance imaging. It operates within three frequency ranges, covering well-known X-atomic nuclei, such as 1H, 13C, 19F, 23Na, and 31P, at both 3T and 7T magnetic field strengths. For the 7T application, the switch simultaneously covers the corresponding X-atomic nuclei frequencies within two bands. However, for the 3T application, tuning is required to cover the corresponding X-atomic nuclei frequencies, particularly those below 60 MHz. The microstripline trace widths of the proposed switch were designed to prevent excessive temperature increases when exposed to 1 kW power of radio frequency pulses. The characteristics of the switch were obtained using the Momentum tool—an electromagnetic simulator supported by ADS software. The proposed switch was fabricated, with its measurement verification results demonstrating favorable return loss (>10 dB), high isolation (>40 dB), and low insertion loss (

Ground-penetrating radars (GPR) based on a variety of techniques have been proposed to improve the performance of buried target (e.g., landmines, threat devices) detection. However, the small radar cross section (RCS) of small electronic devices poses difficulties for target detection, especially when they are buried in lossy and inhomogeneous media. This paper presents a novel buried nonlinear target detection method based on the decomposition of the time-reversal operator (DORT) that uses a multistatic system to overcome the limitations of conventional GPR. Using harmonic radar, which detects the harmonic responses scattered from electronic devices, and DORT processing, which enables focusing/imaging of the detected target, the detection performance is verified by conducting simulation and measurements. The overall results demonstrate that the proposed method achieves accurate detection of buried targets with small RCS.

Inverse synthetic aperture radar (ISAR) image registration enables the analysis of target dynamics by comparing registered images from different viewpoints. However, it faces significant challenges due to various factors, such as the complex scattering characteristics of the target, limited availability of information, and additive noise in ISAR images. This paper proposes a novel ISAR image registration method based on line features. It integrates information from both dominant scatterers and the target’s outer contour to detect lines. According to the consistency principles of multiple lines in rotation and translation, line features from different ISAR images are matched. Simultaneously, the results of the feature matching are utilized to guide the parameter configuration for optimizing the image registration process. Comparative experiments illustrate the advantages of the proposed method in both feature extraction and registration feasibility.

This paper presents a large-scale base station antenna assembly structure that is low-cost, reliable, and easy to manufacture. The antenna element is composed of a low-loss liquid crystal polymer based on a plastic molded module and a modified wideband stacked patch antenna. The base station antenna consists of a 4 × 8 antenna module, with each module comprising 3 × 1 subarrays along with dual-polarized antenna elements. The antenna element achieved an efficiency of 91% and an impedance bandwidth of 1.14 GHz within a height of 10 mm at 3.7 GHz. Furthermore, the fabricated array antenna structure was tested and verified to have an effective isotropic radiated power of 75.6 dBm at boresight and a steering range of less than ±60°. Therefore, the proposed structure meets the required antenna and beam-forming performance for commercial 5G active antenna systems.

Recent advances in NMR fragment screening use sample illumination to boost NMR sensitivity, reduce measurement time to a few seconds, and reduce sample concentration to a few micromolars. Nevertheless, the absence of a fully automated solution to measure several hundreds of samples with photoinduced hyperpolarization limits the large-scale applicability of the method. We present a setup to couple an optical fiber with a cryogenic probe using the flow-cell accessory port. This setup is compatible with commercially available autosamplers, enabling the fully automated measurement of several hundreds of samples per day.

With the widespread application of Synthetic Aperture Radar (SAR) images in ship detection and recognition, accurate and efficient ship classification has become an urgent issue that needs to be addressed. In few-shot learning, conventional methods often suffer from limited generalization capabilities. Herein, additional information and features are introduced to enhance the understanding and generalization capabilities of the model for targets. To address this challenge, this study proposes a few-shot ship classification method for SAR images based on scattering point topology and Dual-Branch Convolutional Neural Network (DB-CNN). First, a topology structure was constructed using scattering key points to characterize the structural and shape features of ship targets. Second, the Laplacian matrix of the topology structure was calculated to transform the topological relations between scattering points into a matrix form. Finally, the original image and Laplacian matrix were used as inputs to the DB-CNN for feature extraction. Regarding network architecture, a DB-CNN comprising two independent convolution branches was designed. These branches were tasked with processing visual and topological features, employing two cross-fusion attention modules to collaboratively merge features from both branches. This approach effectively integrates the topological relations of target scattering points into the automated learning process of the network, enhancing the generalization capabilities and enhancing the classification accuracy of the model. Experimental results demonstrated that the proposed approach obtained average accuracies of 53.80% and 73.00% in 1-shot and 5-shot tasks, respectively, on the OpenSARShip dataset. Similarly, on the FUSAR-Ship dataset, it achieved average accuracies of 54.44% and 71.36% in 1-shot and 5-shot tasks, respectively. In the case of both 1-shot and 5-shot tasks, the proposed approach outperformed the baseline by >15% in terms of accuracy, underscoring the effectiveness of incorporating scattering point topology in few-shot ship classification of SAR images.

The current state of intelligent target recognition approaches for Synthetic Aperture Radar (SAR) continues to experience challenges owing to their limited robustness, generalizability, and interpretability. Currently, research focuses on comprehending the microwave properties of SAR targets and integrating them with advanced deep learning algorithms to achieve effective and resilient SAR target recognition. The computational complexity of SAR target characteristic-inversion approaches is often considerable, rendering their integration with deep neural networks for achieving real-time predictions in an end-to-end manner challenging. To facilitate the utilization of the physical properties of SAR targets in intelligent recognition tasks, advancing the development of microwave physical property sensing technologies that are efficient, intelligent, and interpretable is imperative. This paper focuses on the nonstationary nature of high-resolution SAR targets and proposes an improved intelligent approach for analyzing target characteristics using time-frequency analysis. This method enhances the processing flow and calculation efficiency, making it more suitable for SAR targets. It is integrated with a deep neural network for SAR target recognition to achieve consistent performance improvement. The proposed approach exhibits robust generalization capabilities and notable computing efficiency, enabling the acquisition of classification outcomes of the SAR target characteristics that are readily interpretable from a physical standpoint. The enhancement in the performance of the target recognition algorithm is comparable to that achieved by the attribute scattering center model.

The feature extraction capability of Convolutional Neural Networks (CNNs) is related to the number of their parameters. Generally, using a large number of parameters leads to improved feature extraction capability of CNNs. However, a considerable amount of training data is required to effectively learn these parameters. In practical applications, Synthetic Aperture Radar (SAR) images available for model training are often limited. Reducing the number of parameters in a CNN can decrease the demand for training samples, but the feature expression ability of the CNN is simultaneously diminished, which affects its target recognition performance. To solve this problem, this paper proposes a deep network for SAR target recognition based on Attribute Scattering Center (ASC) convolutional kernel modulation. Given the electromagnetic scattering characteristics of SAR images, the proposed network extracts scattering structures and edge features that are more consistent with the characteristics of SAR targets by modulating a small number of CNN convolutional kernels using predefined ASC kernels with different orientations and lengths. This approach generates additional convolutional kernels, which can reduce the network parameters while ensuring feature extraction capability. In addition, the designed network uses ASC-modulated convolutional kernels at shallow layers to extract scattering structures and edge features that are more consistent with the characteristics of SAR images while utilizing CNN convolutional kernels at deeper layers to extract semantic features of SAR images. The proposed network focuses on the electromagnetic scattering characteristics of SAR targets and shows the feature extraction advantages of CNNs due to the simultaneous use of ASC-modulated and CNN convolutional kernels. Experiments based on the studied SAR images demonstrate that the proposed network can ensure excellent SAR target recognition performance while reducing the demand for training samples.

With the rapid development of high-resolution radar imaging technology, artificial intelligence, and big data technology, remarkable advancements have been made in the intelligent interpretation of radar imagery. Despite growing demands, radar image intrpretation is now facing various technical challenges mainly because of the particularity of the radar sensor itself and the complexity of electromagnetic scattering physical phenomena. To address the problem of microwave radar imagery perception, this article proposes the development of the cross-disciplinary microwave vision research, which further integrates electromagnetic physics and radar imaging mechanism with human brain visual perception principles and computer vision technologies. This article discusses the concept and implication of microwave vision, proposes a microwave vision perception model, and explains its basic scientific problems and technical roadmaps. Finally, it introduces the preliminary research progress on related issues achieved by the authors’ group.

Synthetic Aperture Radar (SAR) images are an important data source in microwave vision research; however, computer vision cannot interpret these images effectively based on optical perceptual principles. Therefore, microwave vision, which draws inspiration from human visual perception principles and combines computer vision techniques with electromagnetic physical principles, has become an important research direction in microwave remote sensing. Exploring the cognitive basis for microwave vision is crucial for improving the theoretical system of microwave vision. Therefore, as a preliminary attempt to enhance the theoretical understanding of microwave vision, this paper examines the effectiveness of optical perceptual principles for microwave vision. As a classical visual theory, Gestalt perceptual principles are commonly used for describing the perceptual principles of the human visual system for the external optical world and are a cognitive theoretical foundation of computer vision. In this context, this paper uses SAR images as the research object, focuses on the design process of cognitive psychology experiments, and preliminarily studies the effectiveness of Gestalt perceptual principles for SAR images, including the principles of perceptual grouping and perceptual invariance, exploring the cognitive basis of microwave vision. The experimental results indicate that the Gestalt perceptual principles cannot be directly applied to the algorithm design for SAR images, and the knowledge concepts and visual principles derived from the optical world using the human visual system do not perform well in SAR images. In the future, it will be necessary to summarize the corresponding visual cognitive principles based on the characteristics of microwave images, such as SAR images.