Types Of Jamming Research Articles

Using lightweight denoising network to suppress multiple barrage jamming in range‐Doppler domain

AbstractBarrage jamming technology poses a significant threat to radar detection functions, yet there is limited research on anti‐multiple barrage jamming using single‐channel radar systems. Addressing this gap, this paper proposes a framework for anti‐multiple barrage jamming based on range‐Doppler domain denoising. This framework first performs pulse compression and Doppler processing on the echo signal to enhance the signal‐to‐jamming ratio. The range‐Doppler spectrum data is then input into a deep denoising network to suppress multiple types of jamming. Additionally, the paper proposes a lightweight deep denoising network comprising a feature extraction module, a jamming suppression module, and a feature fusion module. The feature extraction module preliminarily extracts jamming features using multiple layers of depth‐wise and point‐wise convolution. The jamming suppression module removes noise at various resolutions through a lightweight encoding and decoding structure, effectively suppressing the barrage jamming signal. The feature fusion module uses convolution kernels with different sizes to merge the multiple features output by the jamming suppression module. Simulation results demonstrate that the proposed method effectively suppresses ten types of barrage jamming. Furthermore, a measured dataset is constructed to verify the method’s effectiveness.

Radio Signal Modulation Recognition Method Based on Hybrid Feature and Ensemble Learning: For Radar and Jamming Signals.

The detection performance of radar is significantly impaired by active jamming and mutual interference from other radars. This paper proposes a radio signal modulation recognition method to accurately recognize these signals, which helps in the jamming cancellation decisions. Based on the ensemble learning stacking algorithm improved by meta-feature enhancement, the proposed method adopts random forests, K-nearest neighbors, and Gaussian naive Bayes as the base-learners, with logistic regression serving as the meta-learner. It takes the multi-domain features of signals as input, which include time-domain features including fuzzy entropy, slope entropy, and Hjorth parameters; frequency-domain features, including spectral entropy; and fractal-domain features, including fractal dimension. The simulation experiment, including seven common signal types of radar and active jamming, was performed for the effectiveness validation and performance evaluation. Results proved the proposed method's performance superiority to other classification methods, as well as its ability to meet the requirements of low signal-to-noise ratio and few-shot learning.

Development of methods for generating examples that train a neural network to detect and classify interference in the structure of a useful signal

The main purpose of the information transmission system is to transfer useful information between its users reliably and accurately. At the same time, it would be advisable to supplement the functionality of the information transmission system with the ability to analyze interference in the communication channel during the transmission of a message. In the case of analysis of damage to the useful signal, which may occur due to interference of various types in the propagation medium, the information transmission system can use the results of such analysis to improve noise immunity, to collect statistics, to control electromagnetic compatibility. It is known that optimal processing of noise-like signals is carried out using a correlator or a matched filter, but these classical devices might be complemented with a neural network, and then in addition to determining the correlation function, it is the neural network that is able to analyze a signal damaged by interference, as shown by the authors in their previous works. In this case, for the correct training of a neural network, samples of a useful signal damaged by various types of jamming are needed. This article discusses methods for the formation of training images for a neural network designed to determine the period of pulse interference acting on an M-sequence as a known signal. Two techniques are suggested. They are digital modelling and experimental (laboratory) method. The first one is based on the inverting some elements (chips) of the original M-sequence located in certain segments whose lengths are random. The inversion is performed by the program written in C++ language. The second approach involves a special installation that consists of a personal computer, an Arduino microcontroller, a useful signal transmitter, a receiver and a periodic interference generator. The objective of each method is to obtain a matrix of damaged chips in a chain of M-sequences. Such matrix was called an “error profile” by the authors. The characteristics of the error profile obtained for certain interference parameters (jamming pulse period and duration) according to suggested methods can be used as training data for the neural network designed for jamming classification.

LJCD-Net: Cross-Domain Jamming Generalization Diagnostic Network Based on Deep Adversarial Transfer.

Global Navigation Satellite Systems (GNSS) offer comprehensive position, navigation, and timing (PNT) estimates worldwide. Given the growing demand for reliable location awareness in both indoor and outdoor contexts, the advent of fifth-generation mobile communication technology (5G) has enabled expansive coverage and precise positioning services. However, the power received by the signal of interest (SOI) at terminals is notably low. This can lead to significant jamming, whether intentional or unintentional, which can adversely affect positioning receivers. The diagnosis of jamming types, such as classification, assists receivers in spectrum sensing and choosing effective mitigation strategies. Traditional jamming diagnosis methodologies predominantly depend on the expertise of classification experts, often demonstrating a lack of adaptability for diverse tasks. Recently, researchers have begun utilizing convolutional neural networks to re-conceptualize a jamming diagnosis as an image classification issue, thereby augmenting recognition performance. However, in real-world scenarios, the assumptions of independent and homogeneous distributions are frequently violated. This discrepancy between the source and target distributions frequently leads to subpar model performance on the test set or an inability to procure usable evaluation samples during training. In this paper, we introduce LJCD-Net, a deep adversarial migration-based cross-domain jamming generalization diagnostic network. LJCD-Net capitalizes on a fully labeled source domain and multiple unlabeled auxiliary domains to generate shared feature representations with generalization capabilities. Initially, our paper proposes an uncertainty-guided auxiliary domain labeling weighting strategy, which estimates the multi-domain sample uncertainty to re-weight the classification loss and specify the gradient optimization direction. Subsequently, from a probabilistic distribution standpoint, the spatial constraint imposed on the cross-domain global jamming time-frequency feature distribution facilitates the optimization of collaborative objectives. These objectives include minimizing both the source domain classification loss and auxiliary domain classification loss, as well as optimizing the inter-domain marginal probability and conditional probability distribution. Experimental results demonstrate that LJCD-Net enhances the recognition accuracy and confidence compared to five other diagnostic methods.

Anti-interrupted-sampling repeater jamming method based on frequency agility waveform and sparse recovery

Interrupted-sampling repeater jamming (ISRJ) is a type of intra-pulse coherent jamming that poses a significant threat to radar detection and tracking of targets. This paper proposes an ISRJ suppression method based on frequency agile waveform and sparse recovery, starting from the temporal discontinuity and modulation characteristics of ISRJ. This method is particularly suitable for scenarios with high jamming duty ratio (JDR) and high jammer sampling duty ratio (SDR). By dividing the transmitted waveform into sub-pulses with different carrier frequencies and applying a two-round block sparse algorithm, the method accurately recovers three parameters of ISRJ, achieving effective jamming identification, reconstruction, and cancellation. Additionally, a target detection technique based on robust sparse recovery is proposed, significantly improving the stability and accuracy of target detection. Comparative experimental results conducted in three scenarios confirm the effectiveness and superiority of this method under high JDR and SDR conditions.

GNSS blanket jamming classification algorithm based on spatial attention mechanism and residual shrinkage neural network

In the context of an increasingly complex electromagnetic environment, satellite navigation systems have become highly susceptible to jamming. Detecting and classifying jamming has thus become crucial for taking effective anti-jamming measures. This paper addresses the issue that the classification accuracy of blanket jamming declines drastically in low jamming-to-noise ratio (JNR) scenarios. To tackle this challenge, a novel algorithm is proposed that combines the spatial attention mechanism with a residual shrinkage neural network (RSN-SA) to classify ten types of blanket jamming, ranging from single jamming to convolutional compound jamming. Specifically, the proposed algorithm first employs the Fourier Synchrosqueezed Transform to extract time-frequency (TF) domain features from the original jamming signal, generating corresponding TF images. Then, the RSN-SA is employed to identify and classify these images effectively while minimizing the impact of noise-related features. This allows the main parts of the TF images to be focused on, resulting in higher recognition accuracy. Simulation results demonstrate that RSN-SA achieves close to 100% accuracy for six single blanket jamming signals. Moreover, compared with the other five algorithms, RSN-SA effectively enhances the classification accuracy of convolutional compound jamming signals in low JNR scenarios and improves the recognition stability in high JNR scenarios. Overall, the proposed algorithm provides a promising solution for classifying blanket jamming in satellite navigation systems with high accuracy and robustness.

A feature extraction and recognition method for interrupted sampling repeater jamming

In order to achieve anti-jamming, radar needs to identify the jamming signals to distinguish between the target echo signals and the jamming signals. Interrupted sampling repeater jamming (ISRJ) is a widely used jamming type in radar and electronic countermeasures. In this paper, we propose an efficient channel attention (ECA)-based one-dimensional residual neural network method for the ISRJ signal recognition. The joint features based on axially integral bispectrum and square integral bispectrum are used to extract the target echo signal and ISRJ signal. The ECA-based one-dimensional residual neural network is applied to the ISRJ recognition. The experiment results show that the proposed ECA-based one-dimensional residual neural network has better correct recognition rate under low signal-to-noise ratio and can offer 30.17% reduction in timeliness than the improved convolutional neural network-residual network (CNN-ResNet) method.

A method for active deception jamming discrimination based on frequency response

There are many types of radar active deception false target jamming that are highly correlated with the real target. Recognizing the real and false targets under a low Signal-to-Noise Ratio (SNR) is difficult. To solve the above problem, this article proposes a real/false target recognition method based on the features of multi-pulse joint frequency response by analyzing the differences in the scattering characteristics and modeling real target echoes as a synthesis of multi-scattering center echoes. Firstly, in the range-doppler domain, the real and false targets are truncated along the range dimension, and a fast Fourier transform is performed to extract the features of multi-pulse joint frequency response. Then, a two-channel feature fusion network is designed for real and false target recognition. Finally, a Multi-Coherent Processing Interval Joint Decision Method (M−CPIJDM) based on temporal information is proposed to improve the recognition performance. Experiments using the measured data show that the proposed method can well recognize real and false target signals under four jamming backgrounds: distance false target, velocity false target, distance-velocity composite false target, and forwarding dense false target.

Radar active deception jamming recognition based on Siamese squeeze wavelet attention network

AbstractActive deception jamming recognition has gained significant attention as a crucial aspect of modern electronic warfare, and a large quantity of jamming recognition methods based on either artificial or deep learning methods have been proposed to date. In actual complex battlefields, the abundant deceptive jamming signals are extremely difficult to obtain and the deceptive jamming signals leveraged by the adversarial jammers are often significantly influenced by noise. To address these challenges a Siamese squeeze wavelet attention network (SSWAN) for radar active deception jamming recognition method is proposed. By constructing a wavelet attention module (WAM), the fine‐grained time‐frequency texture features of echo and jamming signals are preferably extracted even under prohibitively strong noise scenarios. Specifically, the Siamese structure is employed as the main backbone network to measure the similarity between jamming signals and make full use of the training samples; besides, the squeeze learning module is embedded to maintain lightweight and prevent overfitting. Experimental results demonstrate that at a jamming‐to‐noise ratio (JNR) of −8 dB, the proposed method achieves recognition accuracies above 96.3% for 6‐class and multi‐class combinational active deceptive jamming types. Overall, compared to mainstream deep networks, the proposed method exhibits superior advantages in lower JNR ratio and small samples of actual battlefield scenarios.

Kirigami layer jamming

Variable stiffness materials bridge the desirable qualities of soft and rigid robotic components, forming the basis of new machines that contextually adapt their structural properties. Recent developments have led to the creation of jamming materials: Collections of media that undergo pressure-dependent kinematic and frictional interactions to radically, rapidly, and reversibly change in stiffness. Among the types of jamming, laminar jamming harnesses an ensemble of sheets for stiffness changes. Most implementations of laminar jamming employ unaltered, continuous sheets, though the vast design space of mechanical anisotropy programmed via structured cuts invites a new jamming paradigm: kirigami laminar jamming. Through analytical, experimental, and numerical studies, we show how intentional topological augmentation of laminar jamming sheets through shell, void, and cut geometric primitives can elicit bespoke mechanical responses. In particular, we optimize jamming specimens to enhance the jammed to unjammed flexural stiffness ratio across a range of deflections, which unlocks new move-and-hold capabilities. We also demonstrate pressure-induced shape morphing and both sharp-edged and doubly-curved surface matching with kirigami patterns. Results hold broad significance for robotics, morphing structures, and the systematic design of variable stiffness materials.

High-potential directional spot jammers operating from loitering zones during group protection of aircraft

Articulation of the problem. Arranging protection of an air group by a jammer aircraft operating from the loitering zone. Objective. Evaluating the technical feasibility of implementing protection of an air group by a remote coverage aircraft, tactics of application, evaluation of specifications. Results. This paper analyzes the tactics of application of a remote coverage aircraft (RCA), gives the formulas to calculate the basic specifications to be met by the RCA. Types of jamming are analyzed to select most effective ones. A design concept of the equipment meeting the requirements imposed on aircraft of the given class is suggested. Practical significance. Application of the RCA will remove the need for individual means aboard aircraft of an air group, which in its turn will provide for higher combat load carried by each aircraft of the group. EW specialization of the aircraft will allow all of its capabilities (primarily power ones) to be used to the fullest extent, thus providing for higher effectiveness of counteraction.

Compound radar jamming recognition based on signal source separation

To deal with various jamming signals based on digital radio frequency memories, a compound jamming signal recognition method based on source signal separation is proposed. In order to overcome label limitation of the supervised learning method in the recognition process, this paper puts forward "Separation + Recognition" strategy. Firstly, the received signals of multiple channels are preprocessed, and the number of signal sources is analyzed through the single-source detection algorithm. Then the received compound jamming signals are processed by source separation, and the independent single jamming signals can be obtained. On this basis, a fast signal compensation algorithm is added to compensate different source signals at overlapping time-frequency points, which effectively increases the integrity of the separated signals. The separated jamming signals are then put into the convolutional neural network for recognition, and the specific jamming types in the compound jamming signals can be obtained. It has been proved that the recognition accuracy of five kinds of compound jamming exceeds 90% when the jamming-to-noise ratio is 0 dB.

Rescue technology of the jamming accident for the double-shield TBM in complex geological conditions: A case study

More than 80 TBM jamming accidents had been recorded in the Water Diversion Project from the Hongyan River to the Shitou River in Shan’xi Province, China, resulting in substantial losses. This paper aims to investigate the rescue technology for the jamming accidents of the double-shield TBM in the project through a five-year geotechnical in-situ investigation. Firstly, the engineering background, the geological and hydrological conditions, the construction method, the possible jamming hazards, and the support system is introduced in detail. Then, the jamming accidents of the double-shield TBM are classified into the five kinds in total, namely, the shield jamming accident, the cutter head jamming accident, the jamming accident in the fault fracture zone, the jamming accident in the large-deformation section, and the jamming accident at water-rich and severely weathered broken rock section. And corresponding causes of accidents is analyzed at the same time. Afterward, the rescue technology adopted for the typical jamming accidents is explored, including the small pilot tunnel scheme, the advanced grouting scheme, the advanced large pipe shed scheme, the circular excavation scheme, and the auxiliary tunnel scheme. Finally, the prevent approaches for the jamming accident of the TBM are proposed, hoping to guide the same projects.

Random-Frequency-Coded Waveform Optimization and Signal Coherent Accumulation Against Compound Deception Jamming

Among the various types of jamming, active deception jamming has similar characteristics to the transmitted signal. The jamming seriously affects the detection and tracking of real targets by radar. In recent years, waveform design has become an effective anti-jamming method with the maturity of waveform generator technology. However, most anti-jamming waveforms are only designed to combat a single kind of jamming. To compensate for the shortage of existing waveforms that cannot simultaneously combat compound deception jamming, we construct an interpulse-intrapulse joint frequency coded(I-IJFC) anti-jamming waveform and analyze the anti-jamming process flow. Moreover, to reduce the influence of residual jamming, we propose an intrapulse frequency-coded method based on jamming parameters estimation. The waveform can be used against cross-pulse repeater deception jamming(CRDJ) and interrupted sampling jamming(ISJ). Then, to solve the problem that the existing algorithm cannot realize the coherent accumulation of the proposed waveform, we propose a coherent accumulation algorithm of subpulse rearrangement compensation and nonuniform discrete Fourier transform (SRCNDFT). The simulation examples show that the waveform proposed in this paper performs better against compound deception jamming than existing waveforms, and the proposed algorithm can realize the coherent accumulation of I-IJFC echo signals.

A Sorting Method for Radar Echo Extraction Based on Observation Submatrix

AbstractWith the advancement of active jamming technology, a variety of new types of coherent jamming based on digital radio frequency memory have been proposed and implemented in practice, posing serious threats to modern radar systems due to their flexibility, suppression, and deception characteristics. Hence the research on radar echo extraction is necessary and challenging. In this paper, we focus on removing interference from received time‐frequency overlapping signals,and the anti‐jamming problem is transformed into one linear underdetermined blind source separation model in a dual‐channel receiving scenario. The feasible solution is found by utilizing the observation submatrices to convert the observed signal features to a new representation, which originates from sparse component analysis. Simulations demonstrate that the developed technique outperforms standard interference countermeasures in terms of signal‐to‐interference ratio and target identification performance, which are critical for anti‐jamming.

Joint Optimization of Jamming Type Selection and Power Control for Countering Multifunction Radar Based on Deep Reinforcement Learning

Developing intelligent jamming methods to combat the multi-function radar (MFR) has become a vital task in electronic warfare, because the MFR can tune into different working modes according to surrounding environment information. In this paper, we present a solution to the limitations of conventional jamming methods, including strong prior knowledge dependence and inaccurate selection strategies.Specifically, we study a joint optimization of jamming type selection and power control task (JO-JTSPC) for a general radar countermeasure scenario. In particular, we first model the sequential decision-making task JO-JTSPC as a Markov Decision Process (MDP). Subsequently, considering the differences in the designed action space, we accordingly develop two algorithms, i.e., dueling double deep Q-learning (D3QN) and hybrid proximal policy optimization (HPPO), to solve the optimization problem. Taking into consideration the threat level of various MFR working modes and the corresponding required jamming effect, we elaborately design the reward function of MDP as a weighted summation of the mode switching factor, jamming performance factor and jamming power factor. Further, the learned polices of these algorithms are derived based on the designed reinforcement learning (RL) elements. Extensive simulation results demonstrate that the proposed algorithms can learn highly adaptive polices in the radar countermeasure scenarios and achieve good jamming performance.

Joint Polarization-Space-Time Processing for Mainlobe Jamming via CP Decomposition

This paper deals with the problem of mainlobe jamming suppression for polarization sensitive phased-array radar system via a joint polarization-space-time processing method resorting to tensor decomposition. Firstly, a radar model accounting for one target and multiple mainlobe jammers in the far field is considered, which is equipped with a polarized transmitted antenna and a uniform linear dual-polarization receiving array. Then, exploiting the polarization-space-time characteristics, tensor CANDECOMP/PARAFAC (CP) decomposition is introduced to recast the received signal as a third order tensor consisting of three factor matrices. Further, the target echo and the jamming signal can be obtained via minimizing the Frobenius norm of the approximation error between the tensor and the outer product of the factor matrices. Finally, the effectiveness of the proposed method by numerical simulation is verified, showing its capability to combat various types of mainlobe jamming as well as their compound forms compared with the art competing method.

An Anti-Intermittent Sampling Jamming Technique Utilizing the OTSU Algorithm of Random Orthogonal Sub-Pulses

The utilization of intermittent sampling jamming can engender a lofty verisimilitude false target cluster that exhibits coherence with the transmitted signal. Such an assemblage bears the hallmarks of both suppression jamming and deceitful jamming, capable of inflicting substantial impairment upon the radar, potentially leading to its profound incapacitation. Henceforth, the precise discernment of the target and various forms of intermittent sampling jamming emerges as a novel endeavor. In response to this predicament, this paper posits a pulsed radar waveform featuring intra-pulse random orthogonal frequency modulation (FM) and inter-pulse phase coherence. This innovative approach not only presents formidable challenges for the jammer in acquiring radar waveform parameters, but also bolsters the radar’s low probability of intercept (LPI), while maintaining the phase coherence of sub-pulses between pulses. Furthermore, based on this waveform, the characteristics of the intermittent sampling jamming signal and its differences from the target echo signal are analyzed in the time domain, frequency domain, time-frequency domain, and pulse compression domain. Building upon these findings, this paper proposes the sub-division algorithms for typical types of intermittent sampling jamming under this waveform: the full-pulses multi-level maximum inter-class variance and sub-pulses multi-level maximum inter-class variance anti-intermittent sampling jamming algorithms. Simulation analysis demonstrates that this waveform and the anti-jamming algorithms can accurately identify and effectively counteract different types of intermittent sampling jamming in typical scenarios.

Jammed Keplerian gas leads to the formation and disappearance of spiral arms in a coupled map lattice for astronomical objects

Abstract The formation and disappearance of spiral arms are studied by focusing on jammed Keplerian gas in a coupled map lattice (CML) with a minimal set of procedures for simulating diverse patterns in astronomical objects. The CML shows that a spiral arm is a type of traffic jam, and its motion is governed by both a gas inflow into and outflow from the jam. In particular, we present a new approach to simply and directly evaluating the disappearance of spiral arms, called “light-in and heavy-out”. It is based on the gas flow rate difference between the light inflow and heavy outflow leading to the disappearance of traffic jams. Furthermore, we propose an approximate formula for the remaining lifetime of spiral arms, which is immediately derived from the “light-in and heavy-out” approach without calculating their pattern speeds as in conventional differential rotation. The proposed formula is successfully applied to the CML simulations.

Dynamic adversarial jamming-based reinforcement learning for designing constellations

To resist various types of jamming in wireless channels, appropriate constellation modulation is used in wireless communication to ensure a low bit error rate. Due to the complexity and variability of the channel environment, a simple preset constellation is difficult to adapt to all scenarios, so the online constellation optimization method based on Reinforcement Learning (RL) shows its potential. However, the existing RL technology is difficult to ensure the optimal convergence efficiency. Therefore, in this paper, Dynamic Adversarial Interference (DAJ) waveforms are introduced and the DAJ-RL method is proposed by referring to adversarial training in Deep Learning (DL). The algorithm can converge to the optimal state quickly by self-adaptive power and probability direction of dynamic strong adversary of DAJ. In this paper, a rigorous theoretical proof of the symbol error rate is given and it is shown that the method approaches the mathematical limit. Also, numerical and hardware experiments show that the constellations generated by DAJ-RL have the best error rate at all noise levels. In the end, the proposed DAJ-RL method effectively improves the RL-based anti-jamming modulation for cognitive electronic warfare.