Complex Electromagnetic Environment Research Articles

Federated Target Recognition for Multiradar Sensor Data Security

Single sensor radar can no longer satisfy the increasingly complex electromagnetic environment. More attention is paid to radar sensor networks, which can obtain more information from different nodes and points of view. Moreover, better detection and tracking performance can be achieved through resource sharing and complementary advantages (joint learning). How to improve the utilization efficiency of multiple radar sensors with limited resources is an open problem, which can be transformed into joint learning in scenarios such as limited training data or imbalanced samples. This paper presents a distributed learning model to solve these problems. It has three phases. Self-reweighting loss is developed to dynamically rebalance the gradients of positive and negative samples for each category, after which the imbalance of samples can be alleviated. An image generation technique via target reimaging addresses the problem of limited samples. Self-reweighting loss and image generation are then unified in a federated learning framework. The classifier is adjusted using virtual representations to further improve learning efficiency. Comparative studies on the MSTAR dataset demonstrate the advantages of the proposed method.

Sensitivity of the thermomagnetic instability in superconducting film to magnetic perturbation for electromagnetic interference detection

The time-varying magnetic field with electromagnetic perturbation is regarded as an important parameter for the thermomagnetic stability of superconducting film devices. In this work, using a thermomagnetic model, we investigate the sensitivity of thermomagnetic instability in superconducting films exposed to a linear ramp magnetic field, superposed by the additional AC magnetic perturbation with tunable amplitude and oscillation frequency. Surprisingly, we find that the thermomagnetic instability is a non-monotonic function with the increasing oscillation frequency of the magnetic perturbation, depending on the working temperature and oscillation amplitude. The unexpected non-monotonic sensitivity of the thermomagnetic instability is revealed by the characteristic oscillation of the electric field, which cannot be aggravated by the AC magnetic perturbation at very high frequencies. The findings of this paper demonstrate that the magnetic perturbation at very low or high frequencies is not the main factor that triggers the thermomagnetic instability of superconducting films. Furthermore, using the magnetic moment measurement, we propose possible electromagnetic interference detection by the superconducting film based on such non-monotonic sensitivity of the thermomagnetic instability, which can be used to detect the tunable target electromagnetic interference with characteristic frequency in a complex electromagnetic environment.

Intelligent Radar Jamming Recognition in Open Set Environment Based on Deep Learning Networks

Jamming recognition is an essential step in radar detection and anti-jamming in the complex electromagnetic environment. When radars detect an unknown type of jamming that does not occur in the training set, the existing radar jamming recognition algorithms fail to correctly recognize it. However, these algorithms can only recognize this type of jamming as one that already exists in our jamming library. To address this issue, we present two models for radar jamming open set recognition (OSR) that can accurately classify known jamming and distinguish unknown jamming in the case of small samples. The OSR model based on the confidence score can distinguish known jamming from unknown jamming by assessing the reliability of the sample output probability distribution and setting thresholds. Meanwhile, the OSR model based on OpenMax can output the probability of jamming belonging to not only all known classes but also unknown classes. Experimental results show that the two OSR models exhibit high recognition accuracy for known and unknown jamming and play a vital role in sensing complex jamming environments.

Improved High-Order Cumulant TDE Parameter Accumulation Algorithm for Locating UHF Signals of Pulsed Electromagnetic Source

Ultra-high frequency (UHF) signal detection is one of the most effective methods for spatial orientation and partial discharge fault diagnosis. However, when the background noise, especially Gaussian white noise and narrow-band interference, is very high, or the detection region is remote, location accuracy may decrease. To improve this, a location system based on the improved higher-order cumulant time delay estimation (TDE) method combined with energy accumulation is proposed. In the system, the UHF waves are received by a four-antenna array. Improved fourth-order cumulants with a smoothed coherence transform (SCOT) window are used in the TDE, by which Gaussian white noise and narrow-band interference can be efficiently suppressed. The energy accumulation algorithm is applied to the cross-correlation results, by which the accuracy of TDE can be enhanced. The applicability of the proposed localization algorithm is evaluated by simulation and experiment. The simulation results show that the improved fourth-order cumulant TDE-parameter accumulation algorithm is superior in accuracy to traditional location methods. In the experimental measurements, two partial discharge measurement points located in a complex electromagnetic environment are tested. The results illustrate that the proposed method can effectively suppress Gaussian white and narrow-band noise, and the location results can satisfy accuracy requirements when the measurement point is within 70 m.

Nonlinear Analysis of the Car-Following Model considering Delay under the V2X Environment

This paper proposes a model that analyzes the delay effect in V2X communication. V2X technology in the intelligent transportation system can realize the exchange of information between vehicles, people, and the environment. Still, more V2X links occupy many channel resources, and the occupation of channel resources leads to longer communication delays. Therefore, based on the optimal velocity of the car-following model, we model the channel delay in the V2X environment. The stability analysis results show that delay increases lead to system instability and traffic congestion. However, if more links are established, that is, the number of vehicles in the vehicle group ahead increases, traffic jams are less likely to occur. The numerical simulation results show that the longer the delay, the more serious the traffic congestion. In addition, we find an apparent phase transition region in the numerical simulation results, which is consistent with the theoretical analysis results. Therefore, in the intelligent transportation system, the delay caused by the complex electromagnetic environment is a factor that cannot be ignored, and thus the allocation of spectrum resources needs to be reasonably arranged. Otherwise, the V2X technology leads to traffic congestion.

Fast design technique for thinned linear array with arbitrary grid spacing in the presence of multi-constraint requirements

Global optimization algorithms can be performed to design thinned arrays with various beamshapes. However, they have intensive computational efforts and are therefore limited to small to medium sized array problems. On the other hand, analytical methods do not suffer from this problem, but usually assume an idealized environment, thereby excluding the influence of a complex electromagnetic environment on thinned array designs caused, e.g., by beam-scanning and interference signals. In this paper, a unified framework of the reconfigurable thinned array antenna system is developed and we propose a fast design technique for thinned linear arrays that is capable of dealing with the aforementioned limitations. It is based on the previously published by means of the modified iterative Chirp-Z transform (MICZT) technique, which is herein extended and generalized for linear array thinning with arbitrary grid spacing in the presence of multi-constraint requirements. More specifically, the fast computation of the scannable array factor for a uniformly illuminated thinned linear array is discussed, and beam-scanning thinned arrays with symmetry configuration are also exploited in this paper. In addition, thinned array designs with null placement will be investigated for rejecting multiple sidelobe interference signals. Several representative numerical examples and comparisons with existing methods are presented to validate the effectiveness and efficiency of the proposed technique.

DL-Based Anomaly Detection at the Physical-Layer of Cognitive Radio by Deep Support Vector Data Description

Aiming at achieving anomaly detection at the physical-layer of cognitive radio, a Deep Support Vector Data Description (Deep SVDD) based anomaly detection scheme is proposed in this paper. Specifically, two novel techniques with different sensitivities related to Signal-to-Noise Ratio (SNR)-walls are proposed for two typical application scenarios. On one hand, for studying the optimal boundary of the desired signal in the feature space, an Improved Deep SVDD (IDSVDD) is conceived for extracting low-dimensional features of the samples represented in the time-frequency domain. On the other hand, a joint algorithm of Deep SVDD and Modulation Classification (JDSMC) is developed, which is capable of extracting low-dimensional features utilizing deep Convolutional Neural Networks (CNN). Extensive simulation and experimental results demonstrate that: 1) the detection performance and the real-time performance of the proposed techniques outperform conventional control algorithms. 2) The proposed techniques can effectively detect abnormal signals in complex electromagnetic environments and effectively improve the self-aware ability of Cognitive Radio Network (CRN). 3) Over-the-Air (OTA) measurements using Software Defined Radio (SDR) in the lab validate the proposed techniques’ anomaly detection performance.

Research on the Construction Method in Complex Electromagnetic Environment Based on Composite Signal

Based on new signal generation technologies such as vector signal modulation and arbitrary waveform editing and playback, the transmitter resources are multiplexed by each signal component at the same time or sharing time to generate flexible and dense composite signals and solve the problem of complex electromagnetic environment construction under the limited hardware.

Key Parameters of Electromagnetic Compatibility of On-line Monitoring Sensor Device for Power Converter Valve

Converter valve is the key equipment of power grid, the safety and reliability deeply affect the reliability of power grid. In the industry, infrared and discharge sensors have been widely used to realize on-line condition monitoring. However, devices’ failure and accuracy reduced often happen, and the complex electromagnetic environment caused by converter valve are difficult to resisted. It is necessary to study the electromagnetic interference produced by the converter valve in order to guide the electromagnetic compatibility design of the sensor or monitoring device. Based on the typical electromagnetic interference mode, the interference models of converter valve elements are established, and the calculation methods of EMC of converter valve are proposed. According to FDTD method, the electric field distributions under the operation and impulse voltage are calculated, based on which, the EMC requirements of monitoring device for converter valve are proposed.

Transformer-based radio modulation mode recognition

The wireless communication technology develop rapidly, signal modulation mode technology becomes increasingly important. Therefore, in order to enhance the ability of radio signal modulation mode recognition in the complex electromagnetic environment, a novel radio signal modulation mode recognition method is proposed on account of Transformer network. Firstly, raw data were split with a fixed size window. Then, the segmented data was projected into a vector sequence and fed into the Transformer module to model and mine the relationship between signal waveform and modulation method to achieve identification. Extensive experiments were conducted on the RML2016.10a data set generated by GNU radio in the real system. The experimental results show that the proposed Transformer-B/8×2 achieves optimal results for modulation mode recognition with signal-to-noise from -20dB to 18dB, and recognition accuracy reaches 86.75%, verifying the practicability and validity of the forenamed algorithm.

Fault Detection and Isolation of Load Mutation Caused by Electrical Interference of Single-Shaft Combined Cycle Power Plant

Because the generator power-measuring equipment is often accompanied by electrical interference in a complex electromagnetic environment in an actual thermal power plant, the output signal will change or even distort while it passes through the devices of acquisition and conversion. Several practical cases have found that the abnormal generation change phenomenon, impulse or oscillation caused by electrical interference, has different effects on the load regulation of steam turbines. These faults also exist in combined-cycle power plants (CCPPs). However, the insufficient installed capacity and operating life of CCPPs domestically cause similar load mutation failures that are scarcely found. We had to acknowledge that CCPPs and steam turbine regulation characteristics differ. It is of great value to study the influence of differences in load mutation on the load regulation of single-shaft CCPPs. We extracted the fault characteristics of two sudden load change phenomena using the operation data of an actual steam turbine and analyzed them through simulation. Furthermore, a fault detection and isolation method for sudden load changes in a single-shaft CCPP was proposed and the simulation results verified the method’s effectiveness.

Spectrum situation generation from sparse spatial sampling: model and algorithm

With the increasingly complex electromagnetic spectrum environment and the growing demand for the available spectrum, it is urgent to mine the spectrum situation information of wide geographical areas from limited sensing nodes to maintain the order and security of the electromagnetic spectrum and improve the overall utilization efficiency of spectrum resources. Starting from the multidimensional representations of spectrum situations in time, frequency, and space, this paper discusses and summarizes the model and generation mechanisms of electromagnetic spectrum situations based on sparse spatial sampling. The spectrum situation representation method based on a multivariate function is proposed, and the spectrum situation model, in accordance with the propagation characteristics of different transmitters, is established. This paper analyzes the identifiability and recoverability conditions of the spectrum situation model under sparse spatial sampling, which theoretically guides the reliable generation of the spectrum situation. Finally, the performance characteristics of different spectrum situation generation algorithms are compared and analyzed by simulations.

Effect of mixed magnetic field on physical properties of atmospheric suspended fine particles

In recent years, the fine particles suspended in the atmosphere, especially the particle size less than 10 μm, have caused very adverse effects on the climate environment and human health. However, the current research on the causes of air pollution mainly focuses on human activities and weather conditions. Considering the weak magnetism and chargeability of atmospheric fine particles, this study proposed a new view that space electromagnetic radiation may affect the physical properties of fine particles. We first analyzed the dynamic characteristics of magnetic micro-particle in the electromagnetic environment, and built a simulation model in the COMSOL simulation software. Finally, based on the theoretical analysis and simulation results, we built an experimental verification system to evaluate the effects of electromagnetic radiation on the physical properties of micro-particle, and carried out a 30-day control experiment. The experimental results showed that the physical properties such as particle size distribution, mass concentration, and morphology of suspended fine particles in the atmosphere under electromagnetic radiation are significantly different from those under non-electromagnetic radiation environment. It can be inferred that the aggregation, fusion, and deposition of suspended particles in the atmosphere are closely related to the complex electromagnetic environment in space.

Origami tunable frequency selective fabric and its tuning mechanism

The tunable frequency selective surface can change its resonant frequency to better meet the complex and changeable electromagnetic (EM) environment. The traditional metal frequency selective surface (FSS) has high precision, but it is hard, making it not easy to bend and adapt to the surface. In this paper, an origami tunable frequency selective fabric (oTFSF) is proposed, which is thin, flexible, and bendable with precise resonant frequency adjustment function. The oTFSF is made by folding and ironing a single layer of FSF with a special three-dimensional spatial structure. By changing its state, the angle of the EM wave interacting with conductive units of the oTFSF can be altered to realize the precise adjustment of the resonant frequency. When the folding trace is parallel to the electric field, the three-dimensional structure of the oTFSF can be equivalent to the projected plane structure through simulation. Therefore, multiple oblique incidence situations can be converted into vertical incidence situations. The equivalent EM parameters of the oTFSF can be obtained by particle swarm optimization algorithm (PSO) to analyze the influence of unit size change on oTFSF. The results show that when the folding angle increases, the equivalent inductance (L) decreases, and the equivalent capacitance (C) increases. The oTFSF can be used to prepare intelligent EM functional materials with characteristics of broadband, lightweight, flexible, and adjustable frequency.

Cooperative Multi-Node Jamming Recognition Method Based on Deep Residual Network

Anti-jamming is the core issue of wireless communication viability in complex electromagnetic environments, where jamming recognition is the precondition and foundation of cognitive anti-jamming. In the current jamming recognition methods, the existing convolutional networks are limited by the small number of layers and the extracted feature information. Simultaneously, simple stacking of layers will lead to the disappearance of gradients and the decrease in correct recognition rate. Meanwhile, most of the jamming recognition methods use single-node methods, which are easily affected by the channel and have a low recognition rate under the low jamming-to-signal ratio (JSR). To solve these problems, a multi-node cooperative jamming recognition method based on deep residual networks was proposed in this paper, and two data fusion algorithms based on hard fusion and soft fusion for jamming recognition were designed. Simulation results show that the use of deep residual networks to replace the original shallow CNN network structure can gain a 6–14% improvement in the correct recognition rate of jamming signals, and the hard and soft fusion-based methods can significantly improve the correct jamming recognition rate by about 3–7% and 5–12%, respectively, under low JSR conditions compared with the existing single-node method.

Adaptive intra‐pulse interference waveform generation technique based on Convolutional Neural Network—Deep Neural Network with global priority information of radar signal

To adapt to the complex and changeable electromagnetic environment of radar detection, improve the jamming effect on frequency-agile radar and low probability of intercept radar, and solve the problem of poor jamming effect caused by intra-pulse jamming lagging behind target radar signal, a Convolutional Neural Network—Deep Neural Network jamming waveform generation method, based on prior global information of radar signal, is presented in this study. This proposed method uses two networks to generate the overall interference waveforms. One Convolutional Neural Network—Deep Neural Network derives prior global radar information from local prior radar information, which is used by the other Convolutional Neural Network—Deep Neural Network to generate interference. In the whole scheme, an algorithm to design the intra-pulse interference waveform based on the prior global information of the radar signal is proposed. The algorithm can generate the interference waveform samples from the number and area of multiple false targets and the relative peak between multiple false targets and the real target area by the relevant parameters. Then, an evaluation system (Matched filter, Constant False Alarm Rate Detector, and Distance resolution etc.) is established to evaluate the global effect of the designed interference waveforms. Finally, the time-domain Root Mean Squared Errors is used as the loss function to optimise the training of the network and ultimately achieve the requirement of generating intra-pulse adaptive interference waveforms based on radar signal fragments. The principle of generating interference waveforms is based on various aspects of the evaluation of the interference effect, which enhances the time domain correlation between multiple false target areas, thereby boosting the interference effect on the radar. The experimental results show that the proposed method based on the Convolutional Neural Network—Deep Neural Network can effectively solve the problem of intra-pulse interference lag and small effective interference area after the pulse pressure of the interference waveform matching filter. This study offers certain reference significance for the adaptive interference waveform design based on radar prior global information.

Robust resource allocation algorithm in SWIPT-based D2D networks

To improve the operation lifetime of devices and the anti-interference capability of complex electromagnetic environments in device-to-device (D2D) networks, an energy-efficient maximization resource allocation algorithm was proposed for simultaneous wireless information and power transfer (SWIPT)-based D2D networks. Considering the constraints of users' minimum rate, minimum harvested energy, power splitting, and channel allocation, a robust energy-efficient resource allocation model was formulated by jointly optimizing the transmit power, subchannel coefficients, and the power splitting factor. The original NP-hard problem was converted into a deterministic and convex problem by using Dinkelbach's method and the worst-case approach, where the closed-form solutions were obtained by using the matching theory and Lagrange dual theory. Simulation results demonstrated that the proposed algorithm exhibited better robustness and higher energy efficiency.

Tunable natural resonances via synergistic effects of two phases in Fex(CoyNi1-y)100-x for multi-band microwave absorption

Multi-band microwave absorption is becoming ubiquitous owing to the increasingly complex electromagnetic environment driven by the diversity of electronic devices. However, research on efficient electromagnetic absorbers applicable in both centimeter-wave and millimeter-wave bands to address the electromagnetic interference in 5G networks is highly challenging. In this study, Fex(CoyNi1-y)100-x particles with two phases (face-centered cubic (FCC) and hexagonal close-packed (HCP)) were synthesized and were found to exhibit excellent electromagnetic wave absorption. HCP phase with high magnetocrystalline anisotropy was introduced into FCC phase Fex(CoyNi1-y)100-x, resulting in natural resonances in multi-band frequency. Prominent microwave absorption properties in ultra-wide bandwidth ranging from 6.9 to 39.5 GHz were obtained. The maximum reflection loss (RL) of the Fe23(Co0.5Ni0.5)77 composite film reached −50 dB. Such a remarkable absorption performance is attributed to the synergetic effects of the multiple natural resonances generated by the coexistence of HCP and FCC phases in Fe23(Co0.5Ni0.5)77. Overall, this work is promising for the future design of high-performance microwave absorbing materials in a wide bandwidth.

JR-TFViT: A Lightweight Efficient Radar Jamming Recognition Network Based on Global Representation of the Time–Frequency Domain

Efficient jamming recognition capability is a prerequisite for radar anti-jamming and can enhance the survivability of radar in electronic warfare. Traditional recognition methods based on manually designed feature parameters have found it difficult to cope with the increasingly complex electromagnetic environment, and research combining deep learning to achieve jamming recognition is gradually increasing. However, existing research on radar jamming recognition based on deep learning can ignore the global representation in the jamming time–frequency domain data, while not paying enough attention to the problem of lightweighting the recognition network itself. Therefore, this paper proposes a lightweight jamming recognition network (JR-TFViT) that can fuse the global representation of jamming time–frequency domain data while combining the advantages of the Vision Transformer and a convolutional neural network (CNN). The global representation and local information in the jamming time–frequency data are fused with the assistance of the multi-head self-attention (MSA) mechanism in the transformer to improve the feature extraction capabilities of the recognition network. The model’s parameters are further decreased by modifying the standard convolutional operation mechanism and substituting the convolutional operation needed by the network with Ghost convolution, which has less parameters. The experimental results show that the JR-TFViT requires fewer model parameters while maintaining higher recognition performance than mainstream convolutional neural networks and lightweight CNNs. For 12 types of radar jamming, the JR-TFViT achieves 99.5% recognition accuracy at JNR = −6 dB with only 3.66 M model parameters. In addition, 98.9% recognition accuracy is maintained when the JR-TFViT parameter number is further compressed to 0.67 M.

Electromagnetic-Thermal Analysis of FTU under the High-Power Electromagnetic Environment

The feeder terminal unit (FTU) is a critical component of any electric power system that works in a complex electromagnetic environment. However, innovative research of theoretical analysis of electromagnetic–thermal practices inside the FTU system is missing in the literature. This work presents the theoretical methodologies for calculating and analyzing the electromagnetic–thermal influence of FTUs in a high-power electromagnetic (HPEM) environment. The numerical outcomes reveal that the surface of the FTU and connecting cables under powerful electromagnetic pulses (EMP) induce significant electromagnetic fields and high temperatures. Significantly, the electromagnetic pulse and injection of heat through the wires into the FTU may cause interference and possibly damage to the FTU’s circuits and electronics. This work serves as a reference for electromagnetic compatibility (EMC) design of FTUs and provides numerical analysis for ensuring the normal operation of FTU devices under harsh electromagnetic environments.