Complex Electromagnetic Environment Research Articles

Structural design of compressible shape-memory foams for smart self-fixable electromagnetic shielding with reduced reflection

To face the coexistence of the intended and unintended electromagnetic radiation in complex electromagnetic and harsh environment as well as minimize the secondary electromagnetic interference (EMI) pollution, the development of highly designable EMI shields for smart self-fixable EMI shielding with reduced reflection is of great importance. Herein, compressible shape-memory composite foams with strong structural designability have been fabricatd through a simple double dip-coating process, which could not only exhibit excellent shielding effectiveness (SE), but also achieve smart self-fixable SE adjustment under self-fixable compression originating from the ideal thermal/electrical-response shape-memory ability. More importantly, incorporating the concept of structural design, such as the construction of an asymmetric descending gradient structure or reasonable 3D patterns in the foams, could enhance the tunability of EM reflectivity effectively, which contributes to the reduction of EM reflection or even reverse the tendency of EM reflection to increase during the process of self-fixable compression-induced SE adjustment, thus minimizing the aggravated secondary EMI pollution significantly. The strategy and mechanism in this work highlights a significant plateau, on which smart self-fixable EMI shielding behavior with reduced reflection could be realized in highly designable shape-memory foam materials.

SAR Wideband Interference Suppression Method Using Second-Order Multisynchrosqueezing Transform

In the current complex electromagnetic environment, the imaging quality of synthetic aperture radar (SAR) may be severely degraded by wideband interference (WBI), which occupies a wide frequency band overlapping with the frequency band of the SAR system, leading to great difficulties in WBI suppression. To address this problem, we propose a method based on the masked second-order multisynchrosqueezing transform (MSST2) to effectively extract WBI from corrupted SAR data. First, the SAR echo is characterized in the time-frequency (TF) domain by the short-time Fourier transform (STFT) with adaptive window width, and a TF mask for the WBI is generated. Then, the energy of the WBI is concentrated with high resolution by MSST2, and the TF mask is refined according to the MSST2 result. Finally, the WBI is accurately reconstructed from the MSST2 result masked by the refined TF mask and is subtracted from the SAR echo. Abundant experimental results for measured SAR data with simulated and real WBI demonstrate the superior performance and practicability of the proposed method.

Anti-electromagnetic interference method based on high-order cumulant method and PRI sorting

Facing the increasingly complex electromagnetic environment, how to make the detection instrument still have better detection performance is one of the current research hotspots. This paper proposes an anti-electromagnetic interference method based on high-order cumulant and PRI sorting. The former identifies the modulation type of the signal, the latter sorts the signals with different modulation parameters of the same modulation type, and finally selects the signal to be processed. The simulation results show that the algorithm is accurate and effective.

Receiver Position Estimation Method for Multitransmitter WPT System Based on Machine Learning

This article proposes a receiver position estimation method suitable for multitransmitter(multi-TX)wireless power transfer systems from a new perspective of machine learning. The novel method uses the voltage and current of the transmitter side, and is realized through self-learning, which can solve the problems of complex electromagnetic environment and slow response in the traditional method. First, the switching criterion of the multi-TX wireless power transfer (WPT) system based on receiver position is established. In addition, the receiver position estimation method is determined by comparing the estimation accuracy of the back propagation (BP) and support vector regression (SVR) algorithms. On this basis, a swithcing control strategy of multi-TX WPT system based on receiver position estimation is further proposed. Finally, the experimental prototype with FPGA controller is designed to verify the correctness of the theoretical analysis. The average relative error of the position estimation method is about 5%, and the system efficiency is stable near 90% with the misalignment conditions.

Few-Shot Learning for Radar Signal Recognition Based on Tensor Imprint and Re-Parameterization Multi-Channel Multi-Branch Model

In an ever-increasingly complex electromagnetic environment, automatic radar signal recognition is becoming vital. Convolutional neural networks have been widely used for radar signal recognition, but deep learning-based algorithms only recognize trained classes. Recognizing novel radar signals with few-shot samples in an open environment is still a challenging research problem. In this letter, a few-shot learning algorithm based on the tensor imprint algorithm and convolutional classification layer is proposed for radar signal recognition, and the proposed convolutional classification layer can avoid spatial information loss caused by the global pooling layer and the fully connected layer. In addition, the lightweight re-parameterization multi-channel multi-branch convolutional neural network (RepMCMBNet) is proposed for feature extraction. The model is trained on a dataset containing 8 types of radar signals, and achieves high recognition accuracy in a test dataset containing 12 types of radar signals. The overall recognition accuracy of the proposed tensor imprint algorithm achieves 93.9% at −6 dB when the number of samples is 5.

Research on Continuous Wave Electromagnetic Effect in Swept Frequency Radar

In order to establish the prediction model of radar equipment in multisource complex electromagnetic environment, the blocking effect and false alarm interference effect caused by single-frequency CW (continuous wave) electromagnetic interference on typical radar equipment are studied. Taking a certain sweep radar as the research object, the equivalent injection test of EMI (electromagnetic interference) is carried out. Based on the theory of radar front door coupling, the interference mechanism of EMI to radar receiver RF front end is revealed, and the variation law of blocking target and false alarm target level is analyzed. The results show that, under the single-frequency CW-EMI, the target echo level decreases with the increase of the interference field strength, and the false alarm level increases with the increase of the interference field strength. The blocking jamming sensitive bandwidth is about f 0 ± 200 MHz , and the false alarm jamming sensitive bandwidth is about f 0 ± 80 MHz .

A Prediction Method of Electromagnetic Environment Effects for UAV LiDAR Detection System

With the rapid development of science and technology, UAVs (Unmanned Aerial Vehicles) have become a new type of weapon in the informatization battlefield by their advantages of low loss and zero casualty rate. In recent years, UAV navigation electromagnetic decoy and electromagnetic interference crashes have activated widespread international attention. The UAV LiDAR detection system is susceptible to electromagnetic interference in a complex electromagnetic environment, which results in inaccurate detection and causes the mission to fail. Therefore, it is very necessary to predict the effects of the electromagnetic environment. Traditional electromagnetic environment effect prediction methods mostly use a single model of mathematical model and machine learning, but the traditional prediction method has poor processing nonlinear ability and weak generalization ability. Therefore, this paper uses the Stacking fusion model algorithm in machine learning to study the electromagnetic environment effect prediction. This paper proposes a Stacking fusion model based on machine learning to predict electromagnetic environment effects. The method consists of Extreme Gradient Boosting algorithm (XGB), Gradient Boosting Decision Tree algorithm (GBDT), K Nearest Neighbor algorithm (KNN), and Decision Tree algorithm (DT). Experimental results show that, comprising with the other seven machine learning algorithms, the Stacking fusion model has a better classification prediction accuracy of 0.9762, a lower Hamming code distance of 0.0336, and a higher Kappa coefficient of 0.955. The fusion model proposed in this paper has a better predictive effect on electromagnetic environment effects and is of great significance for improving the accuracy and safety of UAV LiDAR detection systems under the complex electromagnetic environment on the battlefield.

Radar signal recognition based on triplet convolutional neural network

Recently, due to the wide application of low probability of intercept (LPI) radar, lots of recognition approaches about LPI radar signal modulations have been proposed. However, facing the increasingly complex electromagnetic environment, most existing methods have poor performance to identify different modulation types in low signal-to-noise ratio (SNR). This paper proposes an automatic recognition method for different LPI radar signal modulations. Firstly, time-domain signals are converted to time-frequency images (TFIs) by smooth pseudo-Wigner–Ville distribution. Then, these TFIs are fed into a designed triplet convolutional neural network (TCNN) to obtain high-dimensional feature vectors. In essence, TCNN is a CNN network that triplet loss is adopted to optimize parameters of the network in the training process. The participation of triplet loss can ensure that the distance between samples in different classes is greater than that between samples with the same label, improving the discriminability of TCNN. Eventually, a fully connected neural network is employed as the classifier to recognize different modulation types. Simulation shows that the overall recognition success rate can achieve 94% at − 10 dB, which proves the proposed method has a strong discriminating capability for the recognition of different LPI radar signal modulations, even under low SNR.

LFM/PRBC Modulation Signal Parameter Estimation

Since pseudo-code phase modulation and carrier frequency modulation composite reconnaissance signals have both the good anti-interference capability and the low probability of interception, they have been widely used in electronic reconnaissance systems. This paper proposes a multi-parameter estimation method for pseudo-random binary phase code (PRBC) and linear frequency modulation (LFM) composite modulation signal in a complex electromagnetic environment. Firstly, this paper uses the square operation to remove the code phase information; secondly, we use the delay correlation and two times frequency estimation to estimate the starting frequency and the frequency modulation rate; finally, the wavelet transform algorithm is utilized to estimate the code rate parameter. Through the simulation analysis and full comparison with other methods, we can prove the performance superiority of this method under low signal-to-noise ratios (SNRs).

RRSARNet: A Novel Network for Radar Radio Sources Adaptive Recognition

Radar radio source (RRS) recognition plays an important role in the fields of military electronic support systems (ESM) and civilian autonomous driving. The rapid development of machine learning technology, especially deep learning, has effectively and efficiently improved RRS intelligent recognition performances when operating in the increasingly complex electromagnetic environment. However, the data sampling limitation and computation cost are still severe challenges in real RRS recognition scenarios. In this paper, we propose a novel network based on meta-transfer learning, called RRSARNet, to achieve effective adaptive RRS recognition in the context of low signal-to-noise ratio (SNR). First, by using the short-time Fourier transform, a six-type small samples RRS simulation dataset with different SNR levels is constructed. Then, a novel RRSARNet, based on metric learning, is proposed, which consists of a four-layer embedding module and a four-layer relational module. Finally, the RRS dataset is divided into training, supporting and testing subsets, which are used to train and test the RRSARNet in a meta-transfer learning method. Experiments on the RRS dataset show that the proposed RRSARNet can achieve an overall accuracy (OA) above 96% and 99% when the SNR is above −15 dB and −10 dB, respectively. Even when the SNR is -30 dB, OA can reach more than 70%. For 5-way 1-shot and 5-way 5-shot experiments, the inference time of an image is about 0.043 and 0.140 milliseconds, respectively. Besides, experiments on the RRS simulation dataset and the two benchmark datasets, the RRSARNet performs better or more competitive than many existing state-of-the-art technologies in terms of recognition accuracy.

Receiver Localization Strategy of Wireless Charging System Based on Mutual Inductance Disturbance

In the wireless charging process of electric vehicles, unmanned aerial vehicles (UAVs), inspection robots and other electrical equipment, coil misalignment is inevitable due to human factors, external factors such as airflow during landing and other positioning problems, which severely limits its application. Accurate localization of the receiver coil can effectively reduce or even avoid the misalignment, but the traditional localization techniques may not be suitable for the complex electromagnetic environment of wireless charging system. In this paper, a receiver localization method based on mutual inductance disturbance for wireless power transfer (WPT) system with a relay coil is proposed. This method can achieve two-dimensional (2-D) localization of the receiver coil, and it will be more effective for high-temperature superconducting (HTS) WPT system with larger transmission power. The simulation and experiment are carried out to locate the receiver coil, and the results verify the feasibility of the proposed method.

Switchable Multifunctional Meta-Surface Composed by Dielectric-Metal Hybrid Antenna Array Architecture

Strontium titanate (STO), the dielectric material, has caught the world’s attention due to its outstanding properties, such as high permittivity, high refractive index, and low loss in the terahertz band. Its permittivity is relevant to the environment temperature. Herein, a multifunctional meta-surface composed of a dielectric-metal hybrid antenna array has been demonstrated, which is a single-layer STO elliptic cylinder. On the one hand, when the environment temperature is 300 K, the proposed meta-surface can achieve perfect absorption and polarization conversion in the frequency range from 0.1 to 0.25 THz; particularly, the meta-surface absorptance can reach 99.97% and 99.92% at a frequency of 0.103 and 0.13 THz respectively, and while it is used as a polarization conversion device, the degree of circular polarization and the ellipticity angle can reach 0.986 and 44.5° at a frequency of 0.228 THz. On the other hand, when the environment temperature changes from 300 to 450 K, the absorption peak changes with the temperature, and the average absorptance reaches 96% at resonance frequency. The proposed meta-surface can be applied in many fields, such as optical sensing, imaging, and energy harvesting. Moreover, it provides a potential solution to research the integrated device in a complex electromagnetic environment.

SF 6 decomposition and insulation condition monitoring of GIE: A review

The statistics of the Conference International des Grands Reseaux Electriques (CIGRE) indicate that the operational reliability of SF6 gas-insulated equipment (GIE) is very high; however, the failure rate of the GIE in operation is much higher than that of the IEC standard, and the fault occurs frequently in the GIE at a high voltage level. The reason is due to the complex and strong on-site electromagnetic interference environment and fully enclosed structure of GIE. The key method and technology for effective on-line monitoring and fault diagnosis of GIE are still lacking. Given the partial strong electromagnetic energy and high temperature induced by early latent insulation faults in the equipment, SF6 gas insulation presents different degrees of decomposition. The decomposition products mainly include SO2F2, SOF2, SO2, HF, and H2S. The decomposition characteristics of SF6 are closely related to the property of insulation faults. At present, this area is attracting attention from the power industry and research institutes. This study summarises the current research on SF6 decomposition component analysis (DCA). The content mainly includes the latest progress of SF6 decomposition characteristics and mechanism under fault conditions, and fault diagnosis methods based on decomposition components.

A review of brain-like spiking neural network and its neuromorphic chip research

Under the current situation of the rapid development of brain-like artificial intelligence and the increasingly complex electromagnetic environment, the most bionic and anti-interference spiking neural network has shown great potential in computing speed, real-time information processing, and spatiotemporal data processing. Spiking neural network is the core part of brain-like artificial intelligence, which realizes brain-like computing by simulating the structure of biological neural network and the way of information transmission. This article first summarizes the advantages and disadvantages of the five models, and analyzes the characteristics of several network topologies. Then, it summarizes the spiking neural network algorithms. The unsupervised learning based on spike timing dependent plasticity (STDP) rules and four types of supervised learning algorithms are analyzed. Finally, the research on brain-like neuromorphic chips at home and abroad are reviewed. This paper aims to provide learning ideas and research directions for new colleagues in the field of spiking neural network.

Single‐station radar‐based UAV position estimation in complex urban environments

In the complex electromagnetic environment of cities, detection and positioning of slow and small unmanned aerial vehicles (UAVs) have always been a very challenging research topic. Usually, multiple radar observation stations are required to detect and track targets. Moreover, each radar observation station requires strict synchronisation, and the system is complex and costly. In view of this, under the condition of known prior information of urban geography, this study proposes a method to locate UAVs in complex electromagnetic environments, which use multiple virtual mirror synchronous radars constructed by multipaths and created by the complex buildings in urban environments. First, the simulation model is constructed, and the corresponding simulation parameters are given. Then, the algorithm for solving the single-station radar location is proposed, and the algorithm is verified by the simulation of a ray propagation model. Finally, the proposed algorithm is further verified based on the measured data captured by the radar system in Section 2. The results show that the proposed algorithm based on the single-station radar is able to locate the UAV in both line-of-sight and non-line-of-sight site, and its positioning accuracy is better than 1 m.

Joint transmit filter and sparse array design with transmit subaperturing FDA‐MIMO radar

Increasingly complex electromagnetic environment makes some troubles for a radar system. Improving the performance of the radar detection and suppressing the interferences are important challenges. In this study, joint transmit filters and sparse array design enabling the maximum signal to interference plus the noise ratio (SINR) beamforming at the transmit subaperturing frequency diverse array multiple-input multiple-output (TS-FDA-MIMO) radar is examined. However, the optimisation problem is non-convex. A cycle iteration algorithm is proposed to obtain the optimal transmit filters and sparse array. The total problem is divided into two sub-problems. The first optimisation sub-problem is the sparse design and the Successive Convex Approximation (SCA) with two-dimensional reweighted mixed l1, 2-norm method is introduced to solve the sub-problem. In addition, the transmit filters design is the second optimisation problem. The sub-problem is changed to the semi-definite relaxation (SDR) problem and the Gaussian random method is utilised to obtain the optimal transmit filters. Through the cycle iteration of the two sub-problems, the optimal solutions are obtained. Finally, the simulation results verified the effectiveness of the proposed method.

A Novel Electromagnetic Compatibility Evaluation Method for Receivers Working under Pulsed Signal Interference Environment

For wireless communication systems, receivers usually work under complex electromagnetic environments and are often susceptible to electromagnetic interference (EMI). With the wide application of pulse signals in various fields, the impact of pulse signals on the receivers of communication system has not been extensively studied. On the one hand, the existing receiver electromagnetic sensitivity (EMS) interference coupling effect is mainly analyzed from the perspective of energy only, without considering of different electromagnetic parameter characteristics of pulse signal, such as pulse width, repetition period, duty cycle and so on. On the other hand, there is a lack of quantitative characterization of typical performance indexes of receiver subject to pulsed interference environment, resulting in insufficient reliability and accuracy of receiver interference degree prediction and electromagnetic compatibility (EMC) evaluation. This paper focuses on the EMS interference coupling effect analysis and EMC evaluation method of receiver under pulsed interference environment. First, based on the analysis of the interference mechanism of the pulse signal on receivers, the formula for the bit error ratio (BER) is derived. Then a system model is proposed to verify the theoretical analysis results through numerical simulation. With the established relationship between the parameters of the pulsed interference and the BER performance of the receiver, a novel EMC evaluation method has been proposed. As a practical application example, the evaluation method is verified via a well-designed experiment on BeiDou Navigation Satellite System (BDS). The experiment shows that the observed phenomena are in good agreement with the conclusions of the proposed evaluation method, proving that the method is applicable to the EMC evaluation of receiver under pulsed signal interference environment.

Prospect for Using Low-Element Adaptive Antenna Systems for Radio Monitoring Stations

This article is devoted to the analysis of prospect to apply multifunctional adaptive antenna systems for radio monitoring stations. The review of publications done demonstrates that current antennas that are developed and used in radio monitoring systems to control and measure the parameters of electromagnetic radiation should be applicable to conduct accurate measurements in wide frequency range under the condition of interferences. The analysis shows that modern adaptive antenna systems are mostly developed for radar and telecommunications applications. In this context we consider possible ways to solve the problem of adapting radio monitoring devices to a complex electromagnetic environment using antenna systems with primary processing of received signals . It was found that the developers of the antennas, which are based on adaptive interference suppression methods, focus basically on the development and implementation of adaptation processes, limiting themselves only to solving electromagnetic compatibility problems. In such approach, the functions of direction finding and measurement of radiation field parameters important exactly for radio monitoring systems are mostly ignored. Therefore, this research area opens up a wide field for identifying new possibilities for constructing multifunctional antenna systems. Focusing on this direction of research, we consider as an example the constraction of a simple two-element adaptive antenna system, which can be used to measure the parameters of the electromagnetic field in radio monitoring systems. The main relations for the error of determining the direction of arrival of the interference signal with a simple two-element antenna are investigated. The influence of the stability of the antenna array parameters and functional units of signal processing onto the errors is estimated.

Liquid reconfigurable stealth window constructed by a metamaterial absorber

In this paper, a liquid reconfigurable stealth window constructed by a metamaterial absorber at the microwave band is proposed. The stealth window consists of an anti-reflection glass with indium tin oxide (ITO) as resistive film and a liquid container made of polymethyl methacrylate (PMMA). Since the materials constituting the window are all optically transparent, the metamaterials that can be switched through the liquid control system can always maintain high light transmission. The proposal can obtain a transmission passband from 2.3 GHz to 5 GHz with low insertion loss, especially at 2.45 GHz and 5 GHz with the insertion loss of the passband reaching 0.51 dB and 0.99 dB, by water with 75% ethanol drainage. It can also reflect electromagnetic waves at 2.45 GHz and absorb them from 4.5 GHz to 10.5 GHz with a strong absorptivity over 90% by ethanol injection, exhibiting the reconfigurable electromagnetic characteristic of switching between the transmission state and absorption state. Furthermore, the proposed absorber shows its good transmission/absorption performance. Under the condition of ethanol filling, when electromagnetic waves are incident obliquely at 50°, the absorption rate still exceeds 80%. Finally, the prototype window has been fabricated to demonstrate the validity of the proposed structure, which indicates that the proposal presents significant implications for smart stealth systems and wireless local area network (WLAN) communication that require switching of working states in a complex electromagnetic environment.

Research on the Temperature Field Distribution Characteristics for Thermal Management of Coilgun

Ohmic heating has become one of the main factors restricting their efficiency and longevity of the synchronous induction coilgun (SICG) operating in a complex electromagnetic environment. Therefore, further exploration on the temperature spatial–temporal distribution characteristics of the SICG is of great practical significance to ensure its reliable and efficient operation. The single-stage SICG is taken as the research object to simplify the solving process. First, the effectiveness and accuracy of the numerical simulation models in terms of kinetic and thermodynamic properties are compared and verified. Then, the influence of different structural parameters on temperature rise and acceleration is investigated in detail. The conclusions can not only provide theoretical reference for the long-term service of the SICG but also guide the related research of continuous emission.