Electronic Countermeasures Research Articles

Classification and Identification of Frequency-Hopping Signals Based on Jacobi Salient Map for Adversarial Sample Attack Approach

Frequency-hopping (FH) communication adversarial research is a key area in modern electronic countermeasures. To address the challenge posed by interfering parties that use deep neural networks (DNNs) to classify and identify multiple intercepted FH signals—enabling targeted interference and degrading communication performance—this paper presents a batch feature point targetless adversarial sample generation method based on the Jacobi saliency map (BPNT-JSMA). This method builds on the traditional JSMA to generate feature saliency maps, selects the top 8% of salient feature points in batches for perturbation, and increases the perturbation limit to restrict the extreme values of single-point perturbations. Experimental results in a white-box environment show that, compared with the traditional JSMA method, BPNT-JSMA not only maintains a high attack success rate but also enhances attack efficiency and improves the stealthiness of the adversarial samples.

Joint range and velocity super-resolution estimation with Doppler effects for innovative OFDM-based RFA RadCom system

Conventional radar and communication signals face challenges when integrating for accurate sensing in dense electromagnetic environments, especially in scenarios involving high-velocity targets estimation. To address this issue, we propose the random frequency agile-orthogonal frequency division multiplexing-based radar and communication (RFA-OFDM-based RadCom) signal, a novel framework that combines RFA hopping radar signal and OFDM signal. This framework effectively handles high-velocity Doppler scenarios, enhancing electronic countermeasure capabilities. In high-velocity scenarios, achieving accurate range and velocity estimation is crucial. We introduce a comprehensive received signal model that considers intrapulse and intersubcarrier Doppler effects, often overlooked in traditional high-velocity contexts. The proposed two-phase hierarchical perceptual methodology enables joint super-resolution estimation using the shared signal. We transform the shared signal echo model into a uniform linear array-like model and employ the matrix decomposition algorithm based on bidirectional weighted frequency smoothing (BWFS-MD) for decoherence processing. Subsequently, the estimation of signal parameters via rotational invariance techniques (ESPRIT)-complementary integrated subspace fitting (E-CISF) algorithm accurately estimates joint range and velocity. Meanwhile, the contrastive analysis of the mutual impacts between radar and communication functions is conducted. Theoretical analysis and simulation results robustly validate the superior performance of the proposed BWFS-MD algorithm. Furthermore, considering the precision of joint range-velocity estimation, real-time constraints, and super-resolution capability (which is emphasized), the E-CSIF algorithm demonstrates the best overall performance from a comprehensive perspective.

Multifunctional Metamaterial with Reconfigurable Electromagnetic Scattering Properties for Advanced Stealth and Adaptive Applications.

The rapid development of radar detection systems has led to an increased sensitivity to the electromagnetic (EM) scattering properties of detected targets. Flexible and adaptable EM scattering properties significantly enhance the survivability of battlefield weapons. This paper presents the design of a novel multifunctional metamaterial with reconfigurable EM scattering properties based on a bistable curved beam. In addition to the cushioning and energy absorption properties of curved beams, the metamaterial achieves more than 90% EM absorption in the frequency range of 2.17-17.31GHz, with a relative thickness of only 0.09λL. The bistable nature of the metamaterial allows it to switch between different states. Moreover, combined with the digital coding, this metamaterial can continuously adjust the absorbing bandwidth and further enhance the EM absorption rate within a specific frequency band range. If applied to satellite configurations, the developed metamaterial significantly reduces the radar cross section and offers potential applications in reconfiguring EM scattering properties, when applied to satellite configurations. By actively controller and reconstructing the EM scattering properties at certain frequency points, the metamaterial can achieve camouflage, providing innovative solutions for future stealth technology, electronic countermeasures, and deception jamming in radar detection.

Low probability of interception radar overlapping signal modulation recognition based on an improved you-only-look-once version 8 network

Low probability of interception (LPI) radar is widely used in modern electronic warfare. With the increased radiation sources, multiple signals will arrive simultaneously. The traditional feature extraction method has too many features, which brings great trouble to the subsequent data processing. Most modulation recognition methods based on deep learning only consider the single signal after preprocessing, and the generalization ability is weak. This paper proposes a deep learning solution based on an improved you only look once version 8 (YOLOv8) network with a global attention mechanism (GAM), achieving recognition accuracy over 98% in a -10 dB signal-to-noise ratio (SNR) scenario to address the above problems. We improved the performance of the original network, which can be used in electronic countermeasures.

Mitigation of Suppressive Interference in AMPC SAR Based on Digital Beamforming

Multichannel Synthetic Aperture Radar (MC-SAR) systems, such as Azimuth Multi-Phase Centre (AMPC) SAR, provide an effective solution for achieving high-resolution wide-swath (HRWS) imaging by reducing the pulse repetition frequency (PRF) to increase the swath width. However, in an Electronic Countermeasures (ECM) environment, the image quality of multichannel SAR systems can be significantly degraded by electromagnetic interference. Previous research into interference and counter-interference techniques has predominantly focused on single-channel SAR systems, with relatively few studies addressing the specific challenges faced by MC-SAR systems. This paper uses the classical spatial filtering technique of adaptive digital beamforming (DBF). Considering the Doppler ambiguity present in the echoes, two schemes—Interference Reconstruction And Cancellation (IRC) and Channel Grouping Nulling (CGN)—are designed to effectively eliminate suppressive interference. The IRC method eliminates the effects of interference without losing spatial degrees of freedom, ensuring effective suppression of Doppler ambiguity in subsequent processing. This method shows significant advantages under conditions of strong Doppler ambiguity and low jammer-to-signal ratio. Conversely, the CGN method mitigates the effect of interference on multichannel imaging at the expense of degrees of freedom redundant to Doppler ambiguity suppression. It shows remarkable interference suppression performance under weak-Doppler-ambiguity conditions, allowing for better image recovery. Simulations performed on point and distributed targets have validated that the proposed methods can effectively remove interfering signals and achieve high-resolution wide-swath (HRWS) SAR images.

Interrupted-Sampling Repeater Jamming Countermeasure Based on Intrapulse Frequency–Coded Joint Frequency Modulation Slope Agile Waveform

Interrupted-sampling repeater jamming (ISRJ) is widely used in the field of electronic countermeasures, and can severely affect radar detection. Therefore, the problem of ISRJ suppression is a compelling task. In this paper, we propose an ISRJ suppression method based on an intrapulse frequency-coded joint frequency modulation (FM) slope agile waveform. The intrapulse frequency-coded joint FM slope agile waveform is first designed. The delay inserted between subpulses makes the waveform easy to implement in engineering, and the ambiguity function diagram of the waveform approximates the ideal thumbtack type. Next, the echo slices are classified in the fractional domain utilizing the discontinuity of ISRJ and the focusing property of fractional Fourier transform for chirp signals. Then, the target and interference in the interfered echo slices are reconstructed by compressed sensing, and a time-domain filter is constructed based on interference-free echo slices. Finally, the echo signal after interference suppression is further filtered in the time domain to degrade range sidelobes. Simulation results show that the proposed method can effectively suppress three typical types of ISRJ. Moreover, the probability of target detection after interference suppression exceeds 90% when the jamming-to-signal ratio equals 50 dB.

Deep-Autoencoder-Based Radar Source Recognition: Addressing Large-Scale Imbalanced Data and Edge Computing Constraints

Radar radiation source recognition technology is vital in electronic countermeasures, electromagnetic control, and air traffic management. Its primary function is to identify radar signals in real time by computing and inferring the parameters of intercepted signals. With the rapid advancement of AI technology, deep learning algorithms have shown promising results in addressing the challenges of radar radiation source recognition. However, significant obstacles remain: the radar radiation source data often exhibit large-scale, unbalanced sample distribution and incomplete sample labeling, resulting in limited training data resources. Additionally, in practical applications, models must be deployed on outdoor edge computing terminals, where the storage and computing capabilities of lightweight embedded systems are limited. This paper focuses on overcoming the constraints posed by data resources and edge computing capabilities to design and deploy large-scale radar radiation source recognition algorithms. Initially, it addresses the issues related to large-scale radar radiation source samples through data analysis, preprocessing, and feature selection, extracting and forming prior knowledge information. Subsequently, a model named RIR-DA (Radar ID Recognition based on Deep Learning Autoencoder) is developed, integrating this prior knowledge. The RIR-DA model successfully identified 96 radar radiation source targets with an accuracy exceeding 95% in a dataset characterized by a highly imbalanced sample distribution. To tackle the challenges of poor migration effects and low computational efficiency on lightweight edge computing platforms, a parallel acceleration scheme based on the embedded microprocessor T4240 is designed. This approach achieved a nearly eightfold increase in computational speed while maintaining the original training performance. Furthermore, an integrated solution for a radar radiation source intelligent detection system combining PC devices and edge devices is preliminarily designed. Experimental results demonstrate that, compared to existing radar radiation source target recognition algorithms, the proposed method offers superior model performance and greater practical extensibility. This research provides an innovative exploratory solution for the industrial application of deep learning models in radar radiation source recognition.

Joint Antenna Scheduling and Power Allocation for Multi-Target Tracking under Range Deception Jamming in Distributed MIMO Radar System

The proliferation of electronic countermeasure (ECM) technology has presented military radar with unprecedented challenges as it remains the primary method of battlefield situational awareness. In this paper, a joint antenna scheduling and power allocation (JASPA) scheme is put forward for multi-target tracking (MTT) in the distributed multiple-input multiple-output (D-MIMO) radar. Aiming at radar resource scheduling in the presence of range deception jamming (RDJ), the false target discriminator is designed based on the Cramer–Rao lower bound (CRLB) in terms of the spoofing range, and the predicted conditional CRLB (PC-CRLB) plays a role in evaluating tracking accuracy. The JASPA scheme integrates the quality of service (QoS) principle to develop an optimization model based on false target discrimination, with the objective of enhancing both the discrimination probability of false targets and the tracking accuracy of real targets concurrently. Since the optimal variables can be separated in constraints, a four-step optimization cycle (FSOC)-based algorithm is developed to solve the multidimensional non-convex problem. Numerical simulation results are provided to illustrate the effectiveness of the proposed JASPA scheme in dealing with MTT in the RDJ environment.

Weakly Supervised Transformer for Radar Jamming Recognition

Radar jamming recognition is a key step in electronic countermeasures, and accurate and sufficient labeled samples are essential for supervised learning-based recognition methods. However, in real practice, collected radar jamming samples often have weak labels (i.e., noisy-labeled or unlabeled ones), which degrade recognition performance. Additionally, recognition performance is hindered by limitations in capturing the global features of radar jamming. The Transformer (TR) has advantages in modeling long-range relationships. Therefore, a weakly supervised Transformer is proposed to address the issues of performance degradation under weak supervision. Specifically, complementary label (CL) TR, called RadarCL-TR, is proposed to improve radar jamming recognition accuracy with noisy samples. CL learning and a cleansing module are successively utilized to detect and remove potentially noisy samples. Thus, the adverse influence of noisy samples is mitigated. Additionally, semi-supervised learning (SSL) TR, called RadarSSL-PL-TR, is proposed to boost recognition performance under unlabeled samples via pseudo labels (PLs). Network generalization is improved by training with pseudo-labeling unlabeled samples. Moreover, the RadarSSL-PL-S-TR is proposed to further promote recognition performance, where a selection module identifies reliable pseudo-labeling samples. The experimental results show that the proposed RadarCL-TR and RadarSSL-PL-S-TR outperform comparison methods in recognition accuracy by at least 7.07% and 6.17% with noisy and unlabeled samples, respectively.

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.

Microstructure‐Tuned Amorphous Co2FeGe Nanoflakes for Enhanced Microwave Absorption Via Mechanical Alloying

Herein, a simple strategy for fabricating amorphous Co2FeGe nanoflakes by tuning the milling time with mechanical alloying method to enhance the microwave absorption capabilities is presented. These alloys exhibit soft magnetic properties characterized by high saturation magnetization and low coercivity. The enhancement of polarization and resonance effects, leading to improved magnetic and dielectric loss, is attributed to the refinement of crystalline size and the substantial aspect ratio of flaky particles. The minimum reflection loss reaches −48.6 dB at 4.92 GHz with an effective absorption bandwidth of 2.88 GHz in the C‐band. Due to its high Curie temperature, Co2FeGe exhibits considerable potential for maintaining highly efficient microwave absorption capabilities under high‐temperature conditions, thereby providing a novel perspective and technical means for the development of new high‐temperature‐resistant, high‐performance microwave‐absorbing materials. This is expected to play a significant role in future high‐temperature electronic countermeasure systems.

LPI radar waveform recognition model based on multiple feature image and quasi-residual attention module

Abstract Detecting and classifying the modulation type of the intercepted noisy LPI radar waveform has become a hot topic in the field of Electronic Countermeasures (ECM). In this paper, we propose a recognition model based on multiple feature images (MFI) and convolutional neural networks (CNN) with the quasi-residual attention module (QRAM). The core technologies of this model are divided into two parts. One is the MFI that combines the ambiguity function image (AFI) of the complete signal, covariance matrix image (CMI) of the reconstructed signal, and short-time Fourier transform image (STFTI) of the truncated signal into RGB color image in signal processing as the input for recognition network. The other is CNN with a QRAM recognition network, in which the input is sliced into two mappings called identity mapping and residual mapping to build the quasi-residual module, and the attention module is embedded in each mapping to denoise and enhance the feature. The performance of the MFI-QRAM model is demonstrated by recognizing 12 modulation types of the signals defined in this paper. The simulation experiments show that the model has strong robustness with the number of train sets and the variation of SNR. The overall probability of successful recognition (PSR) is 96.17% when the SNR is −6 dB.

Adversarial training for signal modulation classification based on Ulam stability theory

The modulation classification of radar, communication, and other radiation source signals under complex electromagnetic conditions is an important task in the field of military electronic countermeasures. Deep learning is an important tool for automatic modulation classification (AMC) tasks. However, AMC models based on deep neural networks are easily attacked by adversarial methods such as gradient attacks, and even under small perturbations, the model may exhibit significant differences in prediction. To address adversarial attacks, we proposed a Ulam stability adversarial training method, which could improve the robust accuracy and the natural accuracy of the adversarial training model. We introduced a class of Jensen type Ulam stability theorem, which transforms the Jensen condition into a special class of stability preserving regularization terms for adversarial training models, enhancing the model's adversarial robustness. More importantly, our method provided a framework for improving the stability of adversarial training, which can be combined with existing adversarial training methods to effectively enhance their adversarial defense capabilities. In the experimental section, we compared four types of adversarial training methods on three types of modulation recognition datasets, and the results showed that Ulam adversarial training improved robust accuracy by an average of 4.8% and natural accuracy by an average of 2.2% on three datasets. The difference between the best and final accuracies of the models decreased by an average of 4.5%.

Transfer learning method for specific emitter identification based on pseudo‐labelling and meta‐learning

AbstractSpecific emitter identification (SEI) represents a prominent research direction within the electronic countermeasures domain aimed at discerning carrier identity attributes by analysing subtle radar characteristics. At present, most established SEI techniques assume that both the source and target domain (TD) data adhere to the same distribution. However, this assumption is invalidated by semantic drift which frequently occurs between TD and source domain (SD) samples owing to variations in the collection environment, equipment, and other factors. Considering the aforementioned challenges, this article introduces a transfer learning approach for SEI to leverage pseudo‐label integration within the framework of meta‐learning. This approach employs the bispectral perimeter integral for extracting emitter signal features to construct a feature extractor and basic learner based on CNN13. To label and filter the TD samples, the proposed approach utilises the multiple pseudo‐label serial filtering mechanism, which comprises positive and negative pseudo‐labelling strategies, label uncertainty prediction methods, and hard sample filtering strategies. Ultimately, to address algorithmic real‐time requirements, the labelled TD samples are integrated into the feature extractor and learner of the SD through meta‐learning to facilitate the transfer of TD features to the SD training model. Experimental validation conducted on a real radar dataset demonstrated that the proposed algorithm significantly enhances identification accuracy, exhibiting an improvement from approximately 50% to approximately 90%. Furthermore, the algorithm exhibits a short runtime and robust adaptability, effectively catering to the demands of practical scenarios.

Specific emitter identification based on multiple sequence feature learning.

The specific emitter identification is widely used in electronic countermeasures, spectrum control, wireless network security and other civil and military fields. In response to the problems that the traditional specific emitter identification algorithm relies on a priori knowledge and has poor generalizability, and the existing specific emitter identification algorithm based on deep learning has poor feature selection and the adopted feature extraction network is not targeted, etc., the specific emitter identification algorithm based on multi-sequence feature learning is proposed. Firstly, multiple sequence features of the emitted signal of the communication radiation source are extracted, and these features are combined into multiple sequence features. Secondly, the multiple sequence fusion convolutional network is constructed to fuse and deeply extract the multiple sequence features and complete the classification of individual communication radiation sources through the classifier of neural network. The selected sequence features of this algorithm contain more and more essential RFF information, while the targeted design of the multi-sequence feature fusion learning network can effectively extract the essential RFF information. The results show that the algorithm can significantly improve the performance of SEI compared with the benchmark algorithm, with a recognition rate gain of about 17%.

Metamaterial Absorbers with Archimedean Tiling Structures: Toward Response and Absorption of Multiband Electromagnetic Waves.

With the continuous development of electromagnetic wave-absorbing materials, the design of artificial structures for electromagnetic absorbers based on the concept of metamaterials is becoming more abundant. However, in the design process, it is difficult to further broaden the effective absorption band due to the limitation that the traditional single-size structure responds to electromagnetic waves only in specific frequency bands. Therefore, in this paper, based on the moth-eye bionic hexagonal structure absorber with antireflection performance, an Archimedean tiling structure is designed to optimize it, and through the introduction of a variety of primitives with large differences in dimensions, a multifrequency band-response mechanism is achieved to enhance the multireflection mechanism, which can effectively broaden the absorption band and improve the wave absorption performance. Ultimately, the moth-eye bionic structure absorber optimized by (3.4.6.4) can achieve an effective absorption of 10.26 GHz at a thickness of 2 mm. This work presents a new idea for the design work of electromagnetic wave-absorbing metamaterials, which has a broad application prospect in the aerospace, electronic information countermeasures, communication, and detection industries.

Research on multi-UAV collaborative electronic countermeasures effectiveness method based on CRITIC weighting and improved gray correlation analysis

Multi-UAV cluster technology is a hot topic in modern electronic countermeasures research. Based on the “OODA loop” theory of electronic countermeasures, this paper analyzes the effectiveness evaluation method of multi-UAV collaborative electronic countermeasures. We establish an electronic countermeasures correlation state analysis model based on CRITIC weighting and improved gray correlation theory. This method combines multi-UAV collaborative scene examples and use the gray state correlation analysis method based on the dynamic resolution coefficient. We simulate the effects of electronic countermeasures and various influencing factors on the expected task objectives and verify the effectiveness of the proposed method. The results show that this method can better adapt to the dynamic electronic countermeasures scenarios of multi-UAV clusters and has certain participation value for comprehensively and systematically improving the effectiveness evaluation of electronic countermeasures.

Photonics-based wideband multi-format tunable radar jamming signal generator

A photonics-based radar jamming signal generator is proposed in this paper. By adjusting a polarization controller, interrupted-sampling repeater jamming (ISRJ), comb spectrum modulation jamming (CSMJ), ISRJ and CSMJ composite jamming can be respectively generated after photoelectric conversion. We have experimentally verified the generation of ISRJ signals with duty cycles of 50% and 25%, the generation of CSMJ signals with comb spectrum frequency intervals of 5 MHz and 10 MHz, and the generation of their composite jamming signals in the Ku band. Additionally, the ISRJ and CSMJ composite jamming effect of radar signals in the Ka band has been tested. After matched filtering processing, multiple false targets are produced and the total number of jamming targets can be up to 25, demonstrating significant jamming effects. The proposed structure can generate multi-format jamming signals without using digital radio frequency memory, which is suitable for producing high-frequency and large-bandwidth jamming signals. It can be applied in electronic countermeasure systems.

Signal Separation Method for Radiation Sources Based on a Parallel Denoising Autoencoder

Radiation source signal sorting in complex environments is currently a hot issue in the field of electronic countermeasures. The pulse repetition interval (PRI) can provide stable and obvious parametric features in radiation source identification, which is an important parameter relying on the signal sorting problem. To solve the problem linked to the difficulties in sorting the PRI in complex environments using the traditional method, a signal sorting method based on a parallel denoising autoencoder is proposed. This method implements the binarized preprocessing of known time-of-arrival (TOA) sequences and then constructs multiple parallel denoising autoencoder models using fully connected layers to achieve the simultaneous sorting of multiple signal types in the overlapping signals. The experimental results show that this method maintains high precision in scenarios prone to large error and can efficiently filter out noise and highlight the original features of the signal. In addition, the present model maintains its performance and some robustness in the sorting of different signal types. Compared with the traditional algorithm, this method improves the precision of sorting. The algorithm presented in this study still maintains above 90% precision when the pulse loss rate reaches 50%.

Discrete space-time processing in multifunctional radar systems

In radar systems, all basic tasks, from signal detection to their recognition, are solved under conditions of a priori uncertainty arising, among other things, due to natural or organized interference. The task of maintaining a balance between the speed of the information-measuring and control system and the detail of the assessment of the phase coordinates of targets is of particular relevance in modern on-board multifunctional radar systems of fighter aircraft. In such systems, all basic tasks are solved with strict restrictions on space-time resources. The purpose of the work is to analyze the options for reviewing the airspace that provide the solution of basic tasks in multifunctional radar systems with restrictions on the permissible time of observation of targets and taking into account electronic countermeasures. Based on the analysis performed using performance indicators that ensure the noise immunity of multifunctional radar systems, rational variants of a discrete space-time survey for active phased antenna arrays are proposed. In modern antennas with electromechanical scanning, two space viewing modes are used – continuous direction-finding mode and on-pass tracking. In the continuous direction-finding mode, the detail of the phase coordinate estimates is realized only for a single target, but the secrecy of the actions of the multifunctional radar system is not ensured. In the pass-through tracking mode, a high level of detail is provided for assessing the phase coordinates of targets and the noise immunity of multifunctional radar systems. Therefore, in this mode, it is advisable to evaluate only the noise immunity of multifunctional radar systems against the background of natural interference. The greatest detail of estimates of the phase coordinates of targets and noise immunity are achieved in multifunctional radar systems with active phased antenna arrays, the element base of which allows for a fast discrete space-time overview when all parameters of the electromagnetic wave change, including its polarization, according to pseudo-random laws. The use of modern onboard time standards with stability up to 〖10〗^(-18) in combination with high-speed electromechanical switches will significantly increase the detail of the evaluation of the phase coordinates of targets and noise immunity, including during group actions of aircraft. The proposed review options relate to a new priority scientific and technical direction  correction of techniques (principles, methods and algorithms) for the construction of avionics using technological advances in the development of the element base of active phased antenna arrays.