Complex Electromagnetic Environment Research Articles

Interference Characteristics of Electromagnetic Transient Overvoltage on Secondary Equipment of UHV Fixed Series Capacitors

This manuscript addresses the issue of electromagnetic radiation interference experienced by secondary equipment in ultra-high voltage (UHV) fixed series capacitors (FSCs) under electromagnetic transient overvoltage conditions, which cannot be easily determined. To tackle this, a simulation and analysis method for the electromagnetic interference characteristics of secondary equipment is proposed. First, a primary system simulation model of UHV FSC is established, including modeling the platform’s multi-conductor system. The electromagnetic transient overvoltage signals between the low-voltage busbar and the high-potential platform are then simulated and analyzed under two conditions: spark gap triggering and disconnector operation. Next, a finite element model of secondary equipment is created to simulate and analyze the electric field distribution of different materials in the area of the measuring box. The shielding effectiveness of the measuring box is calculated using the overvoltage signals at the measuring box location as excitation. This method allows for the simulation of the electric field distribution in the measuring box area for different materials and calculates the shielding efficiency of the measuring box. It effectively simulates the complex electromagnetic environment of secondary equipment, assesses the electromagnetic shielding efficiency of the measuring box, and provides a theoretical basis for analyzing and improving the anti-interference characteristics of the measuring box.

“Rigid-Flexible” structured polyimine/graphite felt composites with excellent fire Resistance, electromagnetic shielding and recyclability

With the development of the electronic information, the conductive polymers with high electromagnetic interference (EMI) shielding effectiveness has become a research point. The polyimine material that forms a cross-linking network can rapidly achieve bond exchange at high temperatures, which is becoming a potential substrate for carbon loaded materials. In this work, a novel composite material is successfully prepared by incorporating cost-effective graphite felt (GF) with excellent electrical properties into a biobased polyimine network. Leveraging the exchange properties of dynamic imine bond (−C = N-), the two substrates can be separated by acidolysis, enabling a recycle of the GF efficiently. The electromagnetic shielding effectiveness (EMI SE) of the composite material in the X-band (8.2–12.4 GHz) reaches approximately 46 dB. Additionally, the introduction of highly fire-resistant graphite material significantly enhances the flame retardancy of the composite by reducing the peak heat release rate (HRR) by 47.1 %, which improves the fire safety in complex electromagnetic environments. Herein, we broaden the application of polyimine/graphite composites in the electronic information field and provide a new strategy for the preparation of environmentally friendly composite materials.

X-ray communication system with high-repetition-rate pulsed X-ray source and LYSO(Ce) and YAP(Ce) scintillators

X-ray communication offers significant advantages over traditional microwave methods due to its shorter wavelength and higher theoretical bandwidth, enabling efficient space communication and penetration through complex electromagnetic environments. However, current systems face limitations in X-ray emission modulation and high-precision timing detection. To meet the high-frequency transmission demands of space missions, we developed a pulsed X-ray emission source capable of high-frequency modulation. Additionally, we identified specific scintillators with distinct advantages for different transmission frequency ranges, allowing for performance optimization. Experimental results demonstrated a successful transmission rate of 10 MHz, validating the feasibility of MHz-frequency X-ray communication.

Optimizing the function of aircraft WiFi based on the Internet of Things

Abstract. This paper explores the potential of full-duplex wireless communication technology to mitigate interference in aircraft WiFi systems, thereby enhancing the internet access quality and passenger experience. The study focuses on the application of Internet of Things (IoT) technology to improve the resilience of aircraft WiFi systems against interference. Through a series of MATLAB simulations, the research demonstrates that full-duplex technology can effectively cancel out interference signals, leading to clearer and more stable communication quality. The results show that the combined signal, after cancellation, closely resembles the original communication signal, indicating a significant reduction in interference. Despite the promising results, the study acknowledges limitations such as idealized assumptions in the simulation, hardware and software constraints, and the lack of consideration for the complex electromagnetic environment encountered during actual flight operations. Future research should focus on developing more advanced interference detection and mitigation algorithms, and further investigation into the integration of full-duplex technology with existing aircraft WiFi systems, considering hardware constraints and environmental factors, is necessary to fully realize its potential in enhancing communication performance.

Electrochemically-Switched Microwave Response of MXene in Organic Electrolyte.

The increasingly complex electromagnetic (EM) environment necessitates advanced electrically controllable electromagnetic interference (EMI) shielding materials that can adapt to varying EM conditions. This study develops a flexible electrochemically tunable EMI shielding device based on ultrathin Ti3C2Tx MXene films, exhibiting reversible shielding effectiveness (SE) modulation from 18.9 to 26.2dB in X band at 0.1 and -1.5V. Unlike the previously reported mechanism relying on interlayer spacing adjustments, the work leverages transformations of charging state and surface chemistry for tunability during the electrochemical process. The Ti3C2Tx flake size is also evidenced to play a crucial role, with smaller flakes offering higher absorption modulation despite lower SE modulation, enabling the device with high designability. When integrated with Salisbury screen structure, the device achieves adjustable absorption from 93.560% at 0.1V to 99.996% at -1V, showing a tunable reflection suppression ratio up to 32dB with an effective bandwidth of 4.2GHz. Additionally, incorporating resonant cavity structure enables absorption-dominated (over 90%) microwave-responsive switching at 0.1 and -1.5V. This work highlights significant potential of adaptive EMI shielding materials for applications in smart electronic protection, EM switch, and radar camouflage.

An anti‐interference decision‐making method for communication waveform based on collaborative filtering

AbstractAiming at the problem of efficient and reliable transmission for communication system in complex electromagnetic environment, an anti‐interference decision‐making method for communication waveform based on collaborative filtering (CF) is proposed here. The required minimum signal‐to‐noise ratio to meet a specific bit error rate is adopted as a key indicator to evaluate the performance of certain waveform–channel combinations. The main challenge that is solved is to predict transmission performance ratings that cannot be obtained in practice. With CF of waveform features and interference features, the missing training data can be accurately predicted. The convergence speed and prediction accuracy of the proposed algorithm are verified by simulation, and the influence of different feature quantities and different data deficiency degrees on the stability and accuracy of the algorithm is discussed.

Radar signal modulation identification using global context vision transformer

Abstract The accurate identification of phase-coded radar waveforms is critical in electronic warfare (EW) systems, particularly with the increasing use of low probability of intercept (LPI) radars. However, current methods struggle to reliably recognize these waveforms at low signal-to-noise ratios (SNRs). To address this challenge, we propose an AI-based Global Context Vision Transformer (GC-ViT) model that leverages short-time Fourier transform (STFT) phase spectrum for feature extraction. The GC-ViT model enhances recognition accuracy by incorporating both local and global self-attention mechanisms, enabling more effective identification of phase-coded signals in noisy environments. Experimental results demonstrate that the proposed method achieves approximately 80\% recognition accuracy at an SNR of -12 dB, which significantly outperforms existing techniques. This advancement in radar waveform recognition enhances the situational awareness and decision-making capability of EW systems in complex electromagnetic environments.

Perturbation defense ultra high-speed weak target recognition

Ultra high-speed target recognition in complex electromagnetic environments is a critical and fundamental machine perception issue. It is difficult to ensure privacy protection and intelligent confrontation with centralized training in many practical situations. In this paper, an efficient ultra high-speed target recognition approach, named InVision, for robot systems is proposed using deep trustworthy federated learning (DTFL). InVision is accurate, safe, fast, and robust. Particularly, geometric component transformer (GCT) is presented to significantly promote neural element's complex representation description ability. And an ambiguity-aware cooperative learning (AACL) scheme is developed to relieve the noisy label problem. Moreover, decentralized federated training (DFT) is designed to mitigate the intractable privacy protection problem, to efficiently search for similarities and reduce the representation redundancy. Furthermore, to promote the running speed of the system in real-world environments, a lightweight deep architecture, called Mobile-XB, is developed. Extensive quantitative and qualitative experiments are carried out, and the results demonstrate that InVision greatly outperforms the outstanding comparison methods, establishing efficient connections and extraction, and providing security guarantees.

BiLSTM-Filt: Neural network for radar word segmentation

Radar word extraction is the analysis foundation for multi-function radars (MFRs) in electronic intelligence (ELINT). Although neural networks enhance performance in radar word extraction, current research still faces challenges from complex electromagnetic environments and unknown radar words. Therefore, in this paper, we propose a promising two-stage radar word extraction framework, consisting of segmentation and recognition. To fill the vacancy of radar word segmentation, we establish the mathematical model from the time series analysis viewpoint and design a novel segmentation neural network based on Bi-direction Long Short-Term Memory with a filter module (BiLSTM-Filt). Specific radar word structure characteristics are extracted by training the network and applied for detecting radar words in the pulse train. To further improve segmentation performance, a bounding box regression method is designed to merge information from sub-region structures. Simulation experiments on a typical MFR, Mercury, reveal that the proposed method can outperform the baseline methods within complex electromagnetic environments, containing corrupted environments, various pulse backgrounds, and variable pulse train lengths. Due to the artificial design structure, the proposed method can also make a trial on unknown radar word segmentation.

Optimization of Interference Suppression Algorithm for Polarization Sensitive Conformal Array in Complex Electromagnetic Environment

With the development of modern electronic technology, the electromagnetic environment is becoming increasingly complex, which poses a severe challenge to the normal work of radar, communication and other systems. Polarization sensitive array can more effectively distinguish and suppress interference signals by using polarization information of electromagnetic waves, and improve the signal-to-noise ratio (SNR) and anti-interference ability of the system. Conformal array can be closely attached to the carrier surface, reducing space occupation and improving signal receiving efficiency and accuracy. However, at present, polarization-sensitive conformal arrays still face technical problems in interference suppression, especially in complex electromagnetic environment, how to optimize interference suppression algorithms to improve system performance has become the research focus. In this paper, an adaptive interference suppression algorithm combining spatial-polarization domain processing is proposed. The algorithm digitally processes the received signal, extracts spatial and polarization domain information, constructs the joint feature vector of the signal in spatial and polarization domain, and designs a joint filter in spatial and polarization domain. The minimum mean square error (MMSE) criterion and the least mean square (LMS) algorithm are used to update the weight vector of the filter in real time, enabling the filter to automatically adapt to changes in the electromagnetic environment and achieve optimal interference suppression. Simulation experiments show that the algorithm exhibits good stability and adaptability under different electromagnetic environments, effectively suppressing interference and extracting the desired signal. Compared with traditional interference suppression algorithms based solely on spatial domain, the joint spatial-polarization domain adaptive interference suppression algorithm significantly improves the interference suppression ratio (ISR) and signal-to-interference-plus-noise ratio (SINR) performance indicators, verifying that the introduction of polarization domain information can enhance the effect of interference suppression.

Location of 5G base station antenna in substation taking into account path loss and RF radiation

Aiming at the engineering problem that 5G base station antenna is difficult to locate efficiently in complex electromagnetic environment, a two-stage positioning method of 5G base station antenna substation is proposed, which considers signal path loss and antenna RF radiation. Firstly, the path loss solution model of the 5G base station antenna signal in the substation is established, and the RF radiation solution model generated by the coupling excitation of 5G high-frequency electromagnetic wave and the metal shell of the electrical equipment is constructed based on the big element physical optics method. Secondly, combining the advantages of particle swarm optimization (PSO) and interior point algorithm, the antenna positioning method considering path loss and RF radiation is constructed. Among them, in the first stage, the minimum loss of 5G signal path in the substation is taken as the goal, and the antenna stations in the substation are searched globally to get the optimal pre-selected position. In the second stage, the antenna position is calibrated according to the pre-selected position, so that the interference of RF radiation is minimized, and the final position is output. Finally, a 500 kV 5G substation in Henan Province was taken as the simulation object. Compared with the original scheme, the simulation results ensured the minimum 5G path loss in the substation and took into account the electromagnetic compatibility of the equipment in the substation. The effectiveness of the location strategy for maintaining the safe operation of the substation is verified, and it can provide a reference for the 5G base station antenna positioning project of the substation.

Adaptive stochastic resonance enhanced weak linear frequency modulated signal perception in low signal-to-noise ratio environments

Abstract The linear frequency modulated (LFM) signal has gained extensive utilization in radar, sonar and covert communication system due to its large time-bandwidth product, high-precision distance measurement and low detection probability. The perception of weak LMF signals holds significant importance yet encounters substantial challenges within the increasing complexity of the electromagnetic environments. Consequently, this paper proposes an adaptive stochastic resonance (ASR) enhanced approach for perceiving weak LFM signals in low signal-to-noise ratio (SNR) conditions. This approach initially commences a pioneering study on the quantitative synergistic resonance mechanism among LFM signals, random noise, and nonlinear stochastic resonance (SR) systems. Subsequently, the ASR system’s implementation becomes straightforward through the adaptive adjustment of SR system parameters according to the LFM signal and noise characteristics. This implementation leverages the inherent property of noise energy transfer to signal energy, facilitating the enhancement of ordered weak signals via random noise. Following the enhancement of the output signal by the ASR system, the Wigner–Ville distribution (WVD) transform’s time-frequency concentration property is employed to extract the time-frequency characteristics. Building on the WVD transform, the Radon transform with linear integral projection is applied to further harness the time-frequency domain energy concentration, thereby achieving the effective perception of weak LFM signals. Finally, the effectiveness of the proposed method in improving the perception performance of weak LFM signal in very low SNR conditions is verified through numerical simulations. It is anticipated that this method will have potential application value in fields such as radar, sonar, and communication under complex electromagnetic environments.

Construction and analysis of the EMC evaluation model for vehicular communication systems

With the development of wireless communication technologies, vehicle-mounted wireless communication systems are changing with each passing day, and their EMC has become the key to constraining vehicles to adapt to complex electromagnetic environments. Aiming at the limitations of existing EMC evaluation methods or models for wireless communication systems and the actual needs of EMC evaluation and analysis for vehicular communication systems, a five-level novel evaluation model including working environment, signal spectrum, receiver sensitivity, antenna isolation and communication performance is constructed considering the completeness and accuracy of EMC evaluation for vehicular communication systems. The performance of the constructed model is validated using the vehicular communication system of an armored vehicle as an example. The model can evaluate whether there is interference between the working environment and the signal spectrum of the vehicle-mounted radio station. error in the reduction of the receiver sensitivity between calculation and measurement is 5.8%. The calculated isolation of the vehicle-mounted antenna is in good agreement with the measurements. The modulation mode and coding mode of the vehicle-mounted digital communication system with better performance are simulated and analyzed. When the receiver sensitivity is reduced by 6 dB, the vehicle communication distance is reduced by 50%. The simulation and measurement results show that the proposed model is suitable for the evaluation of electromagnetic compatibility of vehicular communication systems for armored vehicles.

The Research of Intra-Pulse Modulated Signal Recognition of Radar Emitter under Few-Shot Learning Condition Based on Multimodal Fusion

Radar radiation source recognition is critical for the reliable operation of radar communication systems. However, in increasingly complex electromagnetic environments, traditional identification methods face significant limitations. These methods often struggle with high noise levels and diverse modulation types, making it difficult to maintain accuracy, especially when the Signal-to-Noise Ratio (SNR) is low or the available training data are limited. These difficulties are further intensified by the necessity to generalize in environments characterized by a substantial quantity of noisy, low-quality signal samples while being constrained by a limited number of desirable high-quality training samples. To more effectively address these issues, this paper proposes a novel approach utilizing Model-Agnostic Meta-Learning (MAML) to enhance model adaptability in few-shot learning scenarios, allowing the model to quickly learn with limited data and optimize parameters effectively. Furthermore, a multimodal fusion neural network, DCFANet, is designed, incorporating residual blocks, squeeze and excitation blocks, and a multi-scale CNN, to fuse I/Q waveform data and time–frequency image data for more comprehensive feature extraction. Our model enables more robust signal recognition, even when the signal quality is severely degraded by noise or when only a few examples of a signal type are available. Testing on 13 intra-pulse modulated signals in an Additive White Gaussian Noise (AWGN) environment across SNRs ranging from −20 to 10 dB demonstrated the approach’s effectiveness. Particularly, under a 5−way5−shot setting, the model achieves high classification accuracy even at −10dB SNR. Our research underscores the model’s ability to address the key challenges of radar emitter signal recognition in low-SNR and data-scarce conditions, demonstrating its strong adaptability and effectiveness in complex, real-world electromagnetic environments.

Multi‐function radar work mode recognition based on residual shrinkage reconstruction recurrent neural network

AbstractIn modern electronic warfare, multi‐function radar work mode recognition is increasingly crucial. However, the challenges posed by complex electromagnetic environments, such as lost pulses, spurious pulses, and measurement errors, along with the reliance of traditional multi‐task learning strategies on clean samples, make it difficult for existing algorithms to achieve satisfactory recognition performance in real‐world scenarios. To address these issues, this paper introduces a novel residual shrinkage reconstruction recurrent neural network (RS‐RRNN). The network uses a Gated Recurrent Unit as its backbone to extract temporal features and enhances feature extraction by reconstructing the GRU's input, while also reducing dependence on clean samples. These features are then processed through a residual shrinkage structure to reduce noise, which significantly improves the model's robustness in non‐ideal scenarios. Simulations demonstrate that RS‐RNN has better performances in accuracy and robustness than existing networks.

Genome engineering of materials based on Ce doping, high-performance electromagnetic wave absorber for marine environment

Traditional microwave absorbing materials (MAM) are difficult to meet the current increasingly complex electromagnetic environment, MAM began to functionally integrated development to meet the needs of diversified applications. For the high humidity and high salt spray environment of the ocean, the development of electromagnetic absorber integrating microwave absorption (MA) and corrosion protection functions is imminent. In this work, high-quality genes were screened through materials genome engineering, and in-situ doping strategies of Ce gene were designed to synergistically enhance MA properties using composition modulation and structure modulation, the effective absorption bandwidth (EAB) of composite material WC@FCC1 can reach 5.62 GHz at an ultra-thin thickness of 1.66 mm. Thanks to the unique 4f orbital mechanism of Ce element, CeO2 is endowed with excellent redox property, forming an oxide protective film on the surface, which prevents the entry of external corrosive media. The excellent corrosion protection performance of the composite material has been verified through electrochemical testing and molecular dynamics simulation. This work provides new design ideas for high-performance MAM, as well as new strategies and insights for functionally integrated electromagnetic absorber.

AcpAS: An Advanced Circularly Polarized Antenna Structure for an Airborne Relay Communication System

The airborne relay system is an important support for improving the stability of communication systems in complex electromagnetic environments. As a key component of the airborne relay system, the antenna needs to have characteristics such as high gain, dual circular polarization, wide beam coverage, and miniaturization. Based on the septum circular polarizer, this paper proposes a high-performance circularly polarized antenna structure suitable for airborne relay systems, named AcpAS. The structure consists of a coaxial feed port, a coaxial-to-waveguide transition, a septum circular polarizer, and a parabolic metal housing. Based on this structure, two antennas operating at 19.6 GHz to 21.6 GHz and 29.2 GHz to 31.2 GHz are designed in this paper. Simulated and measured results show that the two antennas have dual circular polarization characteristics, with beam coverage range of more than ±55° for gains higher than 0 dBi, and the radiation patterns exhibit good symmetry and wide-beam coverage characteristics.

Radar waveform generative design method

AbstractTo address the issue of radar detection performance degradation caused by interference in complex electromagnetic environments, this paper proposes a generative waveform design method and demonstrates its advantages. This method fully exploits waveform degrees of freedom to achieve a larger design space, generating a multitude of agile waveform parameters within the constraint envelope through algorithmic processes. This approach enables the radar to maintain detection performance in complex electromagnetic environments. Compared with the traditional waveform design method, the simulation experiment verifies the effectiveness of the generative waveform design method.

Super-resolution electromagnetic imaging with an optical frequency comb sampling via multi-dimensional signal separation

Electromagnetic sources show wide distribution, broad frequency coverage, and numerous quantities, posing challenges for traditional sensing techniques to achieve ultra-wideband, large-scale detection and localization. The “electromagnetic eye” imaging technique, inspired by the human eye, utilizes a Luneberg lens and a wideband optoelectronic sensing array as the electromagnetic “lens” and “retina,” respectively. This technique utilizes femtosecond optical pulse sampling reception to down-convert wideband signals, facilitating rapid, large range, and wideband sensing of multiple targets in complex electromagnetic environments. However, the limited aperture of the Luneberg lens results in diffraction-limited blurring, and optical down-conversion may lead to spectral aliasing, causing time-frequency-space overlap and reduced system resolution. In this paper, the frequency variation of the point spread function (PSF) in the wideband degraded images is analyzed, and a multi-dimensional joint super-resolution algorithm is proposed, which involves joint time-frequency-space diagonalization of eigenmatrices based on convolutional mixing array model. The concept is demonstrated through a four-sources imaging simulation achieving 2° resolution, breaking the Rayleigh limit 7.25 times. Furthermore, experimental results show 4-10 GHz imaging breaks the Rayleigh limit 4.5 times.

Multifunctional carbon Fiber@TiO2/C aerogels derived from MXene for pressure tunable microwave absorption

Developing microwave absorption (MA) materials with dynamically tunable performance stands as a pivotal endeavor to fulfill the escalating demands of electronic devices and military equipment in complex electromagnetic environments. This study presents advanced hierarchical CF@TiO2/C aerogels with pressure tunable MA performance, engineered through a novel in-situ oxidation process of MXene on carbon fiber (CF) substrates. This process facilitates the formation of a two-dimensional (2D) TiO2/C hybrid layer, which significantly enhances the polarization loss capabilities of the aerogels while maintaining excellent mechanical resilience. The unique architecture of these aerogels facilitates the modulation of their electrical conductivity and permittivity via pressure, thus enabling dynamically adjustable MA performance. Upon compression (50 %), the CF@TiO2/C aerogels showcase an outstanding effective absorption bandwidth (EAB) of 7.84 GHz, accompanied by a remarkable minimum reflection loss (RLmin) of −74.38 dB. Notably, the EAB of CF@TiO2/C aerogels can be precisely tuned from 4 to 18 GHz (covering the C-band, X-band, and Ku-band), with maximum changes in RLmin values (ΔRLmin) reaching as high as 59.30 dB, indicating substantial tunability under varying pressure. Moreover, the CF@TiO2/C aerogels also demonstrate outstanding thermal insulation and pressure sensing performance, highlighting their potential for multifunctional applications. This research not only sheds light on the intricate interplay between nanostructured materials and electromagnetic waves interactions but also sets a new strategy for the design of multifunctional, tunable microwave absorbers suitable for complex electromagnetic environments in modern electronic and military applications.