Complex Electromagnetic Environment Research Articles

Specific emitter identification unaffected by time through adversarial domain adaptation and continual learning

Timely identifying the emitter of target signals is crucial for communication security in complex electromagnetic environments. Specific emitter identification (SEI) is a technique to identify emitters using the hardware fingerprints of emitters. In this paper, the impact of changes in hardware fingerprints over time on SEI is solved, which significantly deteriorates identification performance. This issue is addressed from two aspects: one is mitigating the impact of these changes, and the other is tracking and adapting to them. For the aspect of mitigating impact, an alternating adversarial domain adaptation (AADA) method is proposed to eliminate the time-varying component in hardware fingerprints. Subsequently, a feature map calculation method using weighted Euclidean distance is designed, preserving the main parameters of feature maps for each emitter. For the aspect of tracking and adapting to changes, a continual learning method was designed based on feature maps of each emitter. This approach incorporates the selective annotation of unlabeled new data with an iterative optimization training process. To validate the effectiveness of the proposed method, we independently collected comprehensive time-variant datasets as well as simpler datasets with varying receivers and environments. The proposed method was tested on these datasets and compared with existing conventional and advanced methods. The experimental results indicate that the proposed SEI method exhibits superior recognition performance. Compared to existing methods, it achieved an average recognition accuracy improvement of over 8% on the time-variant dataset, and demonstrated enhanced robustness against these three types of variations.

Hygienic problems of using terahertz electromagnetic radiation (literature review)

The purpose of the work is to review and analyze domestic and foreign scientific works, systematize the scope of application of terahertz electromagnetic radiation (EMR) to determine hygienic problems in the field of health risk prevention in the development and use of modern radioelectronic devices. The literature search was conducted on the databases: eLibrary, Web of Science, and fifty. During the study of scientific literature, from over fifty works were analyzed, there 36 sources were selected 36 sources corresponded to the purpose of the study. Today, the urgent tasks are to predict the parameters of a complex electromagnetic environment in open areas and inside buildings using mobile communication standards 4, 5 and 6G, scientific justification of hygienic standards for the combined effects of the electromagnetic factor, methodological approaches to monitoring EMR levels, including the development of domestic selective EMR meters in a wide range of frequencies (radio frequency and terahertz ranges)

A novel spike detection model for dynamic stress monitoring of bogie frame

The fatigue evaluation of the bogie frame is an important part of the structural health monitoring of the vehicle. During the dynamic stress monitoring, some signal spikes, which are much larger than the normal fluctuation range due to the interference of the complex electromagnetic environment, affect the accuracy of the structural damage assessment and need to be accurately detected and replaced. Aiming at the drawbacks of traditional detection methods that are overly dependent on engineering experience and not universal, a novel spike detection model is proposed in this paper. By the process of data transformation, spike region features are effectively separated. Based on the isolation forest algorithm, the normalized anomaly score of each point is calculated, and the threshold is determined adaptively. The spike detection rate and damage sensitivity are proposed as the evaluation indices of the detection effect of the method. The results show that the spike detection rate is improved by 7.86% on average, and the damage sensitivity is improved by 15.59% on average. The spike detection model in this paper is significantly improved compared to the existing methods.

A three-band narrow-band terahertz perfect absorber for patch antennas and other sensors

We have studied a multi-mode resonance and actively tunable high-sensitivity terahertz perfect absorber using a microstructured Dirac semimetal. The absorber is composed of gold-dielectric-Dirac semimetal three-layer structure, which has good optical sensing characteristics. The absorber produces three perfect absorption modes (>95%) in the range of 2–6 THz, with a minimum bandwidth (FWHM) of 0.18 THz. We studied the adaptability of the device response mode to the actual complex electromagnetic environment by simulating the deflection incident angle. In addition, we also analyzed the influence of various parameter variables on the absorption performance, which provides a clearer idea and more possibilities for the wider application of the device. In terms of sensing detection, the absorber has a maximum refractive index sensitivity S of 610 GHz/RIU and a maximum figure of merit FOM of 3.4 in the ambient refractive index range of 1 to 1.8. Finally, we also realized the tunable absorption frequency of the absorber in 60–80 meV. We believe that based on the good optical properties of the absorber, the device has potential application value in communication technology, space exploration and so on.

A Generalisable Heartbeat Classifier Leveraging Self-Supervised Learning for ECG Analysis During Magnetic Resonance Imaging.

Electrocardiogram (ECG) is acquired during Magnetic Resonance Imaging (MRI) to monitor patients and synchronize image acquisition with the heart motion. ECG signals are highly distorted during MRI due to the complex electromagnetic environment. Automated ECG analysis is therefore complicated in this context and there is no reference technique in MRI to classify pathological heartbeats. Imaging arrhythmic patients is hence difficult in MRI. Deep Learning based heartbeat classifier have been suggested but require large databases whereas existing annotated sets of ECG in MRI are very small. We proposed a Siamese network to leverage a large database of unannotated ECGs outside MRI. This was used to develop an efficient representation of ECG signals, further used to develop a heartbeat classifier. We extensively tested several data augmentations and self-supervised learning (SSL) techniques and assessed the generalization of the obtained classifier to ECG signals acquired in MRI. These augmentations included random noises and a model simulating MRI specific artefacts. SSL pretraining improved the generalizability of heartbeat classifiers in MRI (F1=0.75) compared to Deep Learning not relying on SSL (F1=0.46) and another classical machine learning approach (F1=0.40). These promising results seem to indicate that the use of SSL techniques can learn efficient ECG signal representation, and are useful for the development of Deep Learning models even when only scarce annotated medical data are available.

Lightweight and compressible PANI/Ti3C2Tx/EVA composite foam for tunable microwave absorption

Dynamic deformation and absorbing regulation are still challenging for designing lightweight and high-efficiency microwave absorbing materials, which are of great indispensability to satisfy changeable and complex electromagnetic environments. Herein, a compressible composite foam with sea-island structure was prepared by loading porous polyaniline (PANI)/Ti3C2Tx composites on ethylene/vinyl acetate (EVA) skeleton via a thermally induced phase separation strategy. The composite foam exhibits a minimum reflection loss (RLmin) of −57.84 dB and an effective absorption bandwidth (EAB, RL ≤ −10 dB) of 1.86 GHz (8.2–10.05 GHz) at 3.4 mm. Notably, the microwave absorption (MA) performance can be precisely regulated by simply adjusting the compression ratio of composite foam. The RL peaks shift towards higher frequencies with increasing compression ratios, originating from changes in the internal conductive network. Even under 30 % compression strain, the composite foam still maintains RLmin of −21.53 dB and broadens EAB up to 2.71 GHz at 2.0 mm. This combination of compression capabilities and dynamically tunable MA performance may expand a novel approach to designing multifunctional polymer-based foam.

Filter cable design with defected conductor transmission structures

Electrical cables, as the industry’s blood vessels and nervous system, require evolving distributed filtering for complex electromagnetic environment adaptability. This article introduces a filter cable design featuring an insulated cylinder coated with a defected conductor transmission structure (DCTS). The DCTS, with a well-designed etched pattern, functions as a boundary condition for transmitting specific frequency electromagnetic waves, similar to a lumped filter circuit. To validate this method, a low-pass filter cable is proposed with six-slot-ring defected structures, utilizing polytetrafluoroethylene as the inner dielectric, encased within a flexible printed circuit board (FPCB)-manufactured DCTS. The proposed cable, with precise dimensions (2.4 mm diameter, 340 mm length), demonstrates minimal insertion loss ( < 0.38 dB below 6 GHz) in the passband and rejection exceeding 23 dB at 7.7-25 GHz in the stopband. Further enhancements achieve attenuation exceeding 50 dB in the stopband (7.1 GHz to 20 GHz). Compared to traditional cables, this filter cable addresses electromagnetic compatibility (EMC) by cutting off the interference coupling path.

Compatible metasurface for ultra-wideband radar and switchable infrared stealth

In response to the rapid advancements in radar detection technology and the widespread deployment of infrared sensors, single-function stealth materials are increasingly challenged to meet the sophisticated demands of concealment within complex electromagnetic environments. As a result, there is a pressing need for research into metamaterial structures that can simultaneously deliver ultra-wideband radar stealth and controllable infrared invisibility. Here, a novel metamaterial structure was proposed and realized, comprising vertically integrated infrared stealth and radar stealth layers, with the aim of accomplishing both ultra-wideband radar stealth and controlled infrared invisibility. Coded units were designed based on the geometric phase modulation mechanism and then arrayed through a random matrix strategy optimized by a genetic algorithm, yielding a radar stealth layer characterized by outstanding properties such as ultra-wideband radar stealth and insensitivity to polarization states. A temperature-adaptive infrared stealth switching function was successfully achieved by incorporating vanadium dioxide, a phase-change material, into the infrared stealth layer, exploiting its insulator-to-metal phase transition at a critical temperature. The fabrication and performance testing of the samples have further validated the practicality and rationality of the design scheme. This work can not only open up innovative pathways for the advancement of multi-band compatible stealth technology but is also of great significance for the application of electromagnetic shielding and stealth technologies in complex settings.

Contact/non-contact control of electromagnetic shielding performances of flexible graphite nanoplatelet/carbonyl iron-polylactic acid/carbonyl iron shape memory composite film

To cope with the complex electromagnetic interference and electromagnetic pollution environment, it is necessary to develop the electromagnetic interference shielding products with adjustable shielding efficiency. One of the solutions is to modulate the shielding efficiency by changing a shape of the products. In this work, the polylactic acid-graphite nanoplatelets Janus films with both high and modulated shielding efficiency were prepared by hot pressing method. The shape-memory organic polymer Polylactic Acid (PLA) was used as the base material, whose morphology can be adjusted by changing the ambient temperature. The highly conductive Graphite Nanoplatelets (GNPs) was used as efficient electromagnetic shielding layer. And, a small amount of carbonyl iron particles (CIP) was added into the Janus films to enhance the conductivity of GNPs layer, while realize the pre-deformation state of PLA layer under the non-contact magnetic force. As the GNPs layer thickness increased, the maximum electromagnetic shielding performance of the Janus film reached around 68 dB in the frequency range of 8–26.5 GHz. The surface temperature of the Janus film can be regulated by applying contact current and non-contact light irradiation, to realize the deformation of the Janus film. The electromagnetic shielding performance of Janus film can be manually adjusted to a certain expected value (10–50 dB) by controlling the applied current and irradiation time. The GNPs-PLA Janus films can be applied in complex electromagnetic environments and have enormous research value and development potential in the field of controllable electromagnetic interference shielding.

Radar Emitter Recognition Based on Spiking Neural Networks

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

Modulated Radio Frequency Stealth Waveforms for Ultra-Wideband Radio Fuzes.

The increasingly complex electromagnetic environment of modern warfare and the proliferation of intelligent jamming threaten to reduce the survival rate of radio fuzes on the battlefield. Radio frequency (RF) stealth technology can fundamentally improve the anti-interception and reconnaissance capabilities of radio fuzes, thereby lessening the probability of them being intercepted, recognized, and jammed by the enemy. In this paper, an RF stealth waveform based on chaotic pulse-position modulation is proposed for ultra-wideband (UWB) radio fuzes. Adding a perturbation signal based on the Tent map ensures that the chaotic sequences have sufficiently long periods despite hardware byte limitations. Measuring the approximate entropy and sequence period shows that the Tent map with the addition of perturbation signals can maintain good randomness under byte constraints, closely approximating the Tent map with ideal precision. Simulations verify that the proposed chaotic mapping used to modulate the pulse position of an ultra-wideband radio fuze signal results in superior detection, anti-interception, and anti-jamming performance.

Miniaturized Hard Carbon Nanofiber Aerogels: From Multiscale Electromagnetic Response Manipulation to Integrated Multifunctional Absorbers

AbstractThe multiscale structural engineering strategy presents a powerful method for tailoring the structural attributes of materials at various levels, enabling the flexible control and manipulation of their electromagnetic properties. Nonetheless, orchestrating the multiscale architecture of polymer‐derived carbon aerogels specifically for microwave absorption poses significant challenges. Herein, aramid‐derived hard carbon nanofiber aerogel microspheres (CNFAMs) featuring a hierarchical skin‐core structure are fabricated through a wet‐spinning technique, combined with reprotonation‐mediated self‐assembly and carbonization processes. The presence of large‐scale voids between neighboring microspheres and the microscale porosity within the microspheres themselves improves impedance matching and promotes microwave reflection and scattering. The distinct graphitic domains and defects serve as pivotal elements for conduction and polarization losses, significantly impacting microwave attenuation. By meticulously tailoring the macroscale dimensions, microscale porous architecture, and nanoscale domains, the optimized CNFAMs demonstrate a remarkable absorption bandwidth of 9.62 GHz at an ultralow filling of 0.97 wt%. Additionally, the implementation of application‐oriented microwave absorption through the innovative integration of polysilsesquioxane‐CNFAMs in a host–guest aerogel is explored. This composite system brings together broadband absorption, superhydrophobicity, thermal insulation, resistance to freezing, and robust tolerance to harsh environments. Such a multifaceted approach is designed to tackle the growing challenges associated with complex electromagnetic environments effectively.

Random Stepped Frequency ISAR 2D Joint Imaging and Autofocusing by Using 2D-AFCIFSBL

With the increasingly complex electromagnetic environment faced by radar, random stepped frequency (RSF) has garnered widespread attention owing to its remarkable Electronic Counter-Countermeasure (ECCM) characteristic, and it has been universally applied in inverse synthetic aperture radar (ISAR) in recent years. However, if the phase error induced by the translational motion of the target in RSF ISAR is not precisely compensated, the imaging result will be defocused. To address this challenge, a novel 2D method based on sparse Bayesian learning, denoted as 2D-autofocusing complex-value inverse-free SBL (2D-AFCIFSBL), is proposed to accomplish joint ISAR imaging and autofocusing for RSF ISAR. First of all, to integrate autofocusing into the ISAR imaging process, phase error estimation is incorporated into the imaging model. Then, we increase the speed of Bayesian inference by relaxing the evidence lower bound (ELBO) to avoid matrix inversion, and we further convert the iterative process into a matrix form to improve the computational efficiency. Finally, the 2D phase error is estimated through maximum likelihood estimation (MLE) in the image reconstruction iteration. Experimental results on both simulated and measured datasets have substantiated the effectiveness and computational efficiency of the proposed 2D joint imaging and autofocusing method.

An Adaptive Radar Target Detection Method Based on Alternate Estimation in Power Heterogeneous Clutter

Multichannel radars generally need to utilize a certain amount of training samples to estimate the covariance matrix of clutter for target detection. Due to factors such as severe terrain fluctuations and complex electromagnetic environments, the training samples usually have different statistical characteristics from the data to be detected. One of the most common scenarios is that all data have the same clutter covariance matrix structure, while different data have different power mismatches, called power heterogeneous characteristics. For detection problems in the power heterogeneous clutter environments, we propose detectors based on alternate estimation, using the generalized likelihood ratio test (GLRT) criterion, Rao criterion, Wald criterion, Gradient criterion, and Durbin criterion. Monte Carlo simulation experiments and real data indicate that the detector based on the Rao criterion has the highest probability of detection (PD). Furthermore, when signal mismatch occurs, the detector based on the GLRT criterion has the best selectivity, while the detector based on the Durbin criterion has the most robust detection performance.

CNN-BiLSTM-DNN-Based Modulation Recognition Algorithm at Low SNR

Radio spectrum resources are very limited and have become increasingly tight in recent years, and the exponential growth of various frequency-using devices has led to an increasingly complex and changeable electromagnetic environment. Wireless channel complexity and uncertainty have increased dramatically, and automated modulation recognition (AMR) performs poorly at low signal-to-noise ratios. It is proposed to use convolutional bidirectional long short-term memory deep neural networks (CNN-BiLSTM-DNNs) as a deep learning framework to extract features from single and mixed in-phase/orthogonal (I/Q) symbols in modulated data. The framework combines the capabilities of one- and two-dimensional convolution, a bidirectional long short-term memory network, and a deep neural network more efficiently, extracting characteristics from the perspective of time and space to enhance the accuracy of automatic modulation recognition. Modulation recognition experiments on the benchmark datasets RML2016.10b and RML2016.10a show that the average recognition accuracies of the proposed model from −20 dB to 18 dB are 64.76% and 62.73%, respectively, and the improvement ranges of modulation recognition accuracy are 0.29−5.56% and 0.32−4.23% when the signal-to-noise ratio (SNR) is −10 dB to 4 dB, respectively. The CNN-BiLSTM-DNN model outperforms classical models such as MCLDNN, MCNet, CGDNet, ResNet, and IC-AMCNet in terms of modulation type recognition accuracy.

Design and analysis of ultra-wideband miniaturized metamaterial absorbers for radiation suppression

This paper presents a novel miniaturized ultra-wideband metamaterial absorber with a thickness of 0.069λ L thick, engineered to suppress electromagnetic radiation in high-frequency chip packaging. The absorber’s bandwidth is enhanced through the incorporation of lumped resistors and patterned metal strips. Simulation results reveal an absorption efficiency exceeding 90% across the frequency range of 17.3–33.5 GHz, while maintaining polarization insensitivity and angular stability. The absorption mechanism is investigated using equivalent circuit theory and the S-parameter inversion method, which demonstrate consistent impedance matching and advantageous loss characteristics throughout the wide band. Relevant objects were meticulously prepared and tested, yielding a high correlation between the measured and simulated results. The proposed absorber, when integrated into package chips and patch antennas, significantly reduces the far-field electric field amplitude in both applications. Specifically, the packaged chip shows up to 19.7 dB of electromagnetic radiation suppression and the patch antenna structure achieves over 10 dB of radiation suppression in the 24.2 GHz–30.4 GHz range post-integration. These results affirm the absorber’s efficacy in effectively reducing unwanted electromagnetic radiation in specified frequency bands within complex electromagnetic environments. The absorber’s demonstrated effectiveness provides a promising approach to address the increasing issue of electromagnetic radiation in progressively miniaturized electronic devices.

Radar signal sorting based on image semantic segmentation

Abstract This paper explores the application of image semantic segmentation networks in radar signal sorting. Unlike traditional methods, it does not depend on predefined parameters or prior information, so it can show enhanced adaptability in dealing with a more complex electromagnetic environment. Firstly, the pulse descriptor PDW of collected signals is encoded into corresponding sequence images, and then U-net is used to train pulse sequence images. The trained model can be applied with high accuracy in radar signal sorting. At the same time, an attention mechanism is introduced into the U-net, which improves the recognition rate of signal features.

Joint Wideband Beamforming Algorithm for Main Lobe Jamming Suppression in Distributed Array Radar

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

Specific emitter identification by wavelet residual network based on attention mechanism

AbstractSpecific emitter identification technology plays a very important role in spectrum resource management, wireless network security, cognitive radio etc. However, in complex electromagnetic environments, the variability and uncertainty of signals make it very difficult to extract representative feature representations of the signals. At the same time, the feature extraction capability of the recognition model is also a factor that needs to be considered. To address these issues, a wavelet residual neural network model based on attention mechanism is proposed for specific emitter identification. First, multi‐level wavelet decomposition is performed on all received signals to obtain their wavelet detail coefficients at different scales. Next, all the wavelet detail coefficients are used as the feature input for the attention‐based residual network, and perform parallel feature extraction at multi scales. Finally, the feature representation capability of all coefficients are compared, and the model's recognition results based on it are obtained. The recognition rates on the three datasets are 94.7%, 93.21%, and 86.1%, respectively, all of which are superior to recent state‐of‐the‐art algorithms. In addition, through ablation experiment, the validity of each component of the model has been verified.

An Embedded Electromyogram Signal Acquisition Device.

In this study, we design an embedded surface EMG acquisition device to conveniently collect human surface EMG signals, pursue more intelligent human-computer interactions in exoskeleton robots, and enable exoskeleton robots to synchronize with or even respond to user actions in advance. The device has the characteristics of low cost, miniaturization, and strong compatibility, and it can acquire eight-channel surface EMG signals in real time while retaining the possibility of expanding the channel. This paper introduces the design and function of the embedded EMG acquisition device in detail, which includes the use of wired transmission to adapt to complex electromagnetic environments, light signals to indicate signal strength, and an embedded processing chip to reduce signal noise and perform filtering. The test results show that the device can effectively collect the original EMG signal, which provides a scheme for improving the level of human-computer interactions and enhancing the robustness and intelligence of exoskeleton equipment. The development of this device provides a new possibility for the intellectualization of exoskeleton systems and reductions in their cost.