Active Jamming Research Articles

A rapid recognition method for radar active jamming based on the Hybrid 3_CNN‐Transformer model

A rapid recognition method for radar active jamming based on the Hybrid 3_CNN‐Transformer model

MTD precise jamming technology based on intra-pulse and inter-pulse frequency-phase joint modulation

For moving target detection (MTD) radar, existing jamming systems face problems such as poor controllability and strong regularity of two-dimensional false target characteristics. Time-frequency-phase modulation technology has great potential for active jamming and has received widespread attention. In this article, we propose a precise jamming method based on intra-pulse and inter-pulse frequency-phase joint modulation to control the two-dimensional distribution of false targets. First, construct periodic phase and segmented frequency modulation functions. Secondly, the intra-pulse phase modulation period Ts1 and frequency modulation amount ξl1 accurately control the positions of multiple false targets; the inter-pulse phase modulation period Ts2 and frequency modulation amount ξl2 accurately control the velocity of multiple false targets. Finally, the amplitude of the two-dimensional false target is accurately controlled by adjusting the phase modulation coefficient φi, phase modulation and frequency modulation duty cycles ηs, ηl. Compared with other jamming algorithms, the jamming distribution is more flexible and controllable, which verifies the superiority of this method.

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.

Active jamming at criticality

Jamming is ubiquitous in disordered systems, but the critical behavior of jammed solids subjected to active forces or thermal fluctuations remains elusive. In particular, while passive athermal jamming remains mean-field-like in two and three dimensions, diverse active matter systems exhibit anomalous scaling behavior in all physical dimensions. It is therefore natural to ask whether activity leads to anomalous scaling in jammed systems. Here, we use numerical and analytical methods to study systems of active, soft, frictionless spheres in two dimensions, and elucidate the universal scaling behavior that relates the excess coordination, active forces or temperature, and pressure close to the athermal jammed point. We show that active forces and thermal effects around the critical jammed state can again be captured by a mean-field picture, thus highlighting the distinct and crucial role of amorphous structure in active matter systems. Published by the American Physical Society 2024

Deep Learning-Based Active Jamming Suppression for Radar Main Lobe

Due to the development of digital radio frequency memory (DRFM), active jamming against the main lobe of the radar has become mainstream in electronic warfare. The jamming infiltrates the radar receiver via the main lobe, covering up the target echo information. This greatly affects the detection, tracking, and localization of targets by radar. In this study, we consider jamming suppression based on the independence of RF features. First, two stacked sparse auto-encoders (SSAEs) are built to extract the RF characteristics and signal features carried out by the actual radar signal for subsequent jamming suppression. This method can effectively separate RF features from signal features, making the extracted RF features more efficient and accurate. Then, an SSAE-based jamming suppression auto-encoder (JSAE) is proposed; the mixed signal, including the radar signal, jamming signal, and noise, is input to JSAE for dimensionality reduction. Therefore, the radar signal and RF features, extracted by the two SSAEs in the previous step, are used to constrain the features of the reduced mixed signal. Moreover, we integrate the feature level and signal level to jointly achieve jamming suppression. The original radar signal is used to assist the radar signal reconstructed by the decoder. By first filtering out interference-related features and then reconstructing the signal, we can achieve better jamming suppression performance. Finally, the effectiveness of the proposed method is verified by simulating the actual collected data.

RIS-Assisted Secure UAV Communication Scheme Against Active Jamming and Passive Eavesdropping

RIS-Assisted Secure UAV Communication Scheme Against Active Jamming and Passive Eavesdropping

A complete solution for anti-jaming radio data-link of an unmanned aerial vehicle

The effectiveness of an unmanned aerial vehicle's flight mission over a given distance depends on many factors. However, one of the key factors in the successful completion of a flight mission is the availability of high-quality management and control of telemetry by an unmanned aerial vehicle, which in turn is determined by a reliable radio communication between the ground control station and the unmanned aerial vehicle. The current stage of use of unmanned aerial vehicles is characterised by the use of active jammers of various types and purposes, the purpose of which is to disable control channels, navigation channels, telemetry channels and data transmission channels of FPV unmanned aerial vehicles in order to prevent the successful completion of the flight mission of the vehicle. The article considers conceptual issues of implementing a guaranteed interference-free radio data transmission and control of an unmanned aerial vehicle in the conditions of active jamming interference when combining various technological solutions. These solutions include the selection and justification of a special frequency range, the use of structural circuit solutions for interference protection of the radio data transmission and control line; the use of a specific scheme for organising communication with an unmanned aerial vehicle; the use of a transceiver and portable and light antennas for the radio channel that meet reasonable technical requirements; the implementation of the possibility of real-time monitoring of the interference situation on board the unmanned aerial vehicle and detection of the type of interference; ensuring flight in the sector. The requirements were justified and options for selecting a transceiver and antenna were proposed. A structural diagram of the radio line is developed and options for its use as a data transmission and control radio line for an unmanned aerial vehicle are proposed. Examples of real-time tracking of the interference situation on board an unmanned vehicle and detection of the type of interference are given. A flight option in the sector without the use of antenna tracking is calculated and justified. The approximate values of the signal at the receiver input of the unmanned vehicle are calculated.

Radar Active Jamming Recognition under Open World Setting

To address the issue that conventional methods cannot recognize unknown patterns of radar jamming, this study adopts the idea of zero-shot learning (ZSL) and proposes an open world recognition method, RCAE-OWR, based on residual convolutional autoencoders, which can implement the classification of known and unknown patterns. In the supervised training phase, a residual convolutional autoencoder network structure is first constructed to extract the semantic information from a training set consisting solely of known jamming patterns. By incorporating center loss and reconstruction loss into the softmax loss function, a joint loss function is constructed to minimize the intra-class distance and maximize the inter-class distance in the jamming features. Moving to the unsupervised classification phase, a test set containing both known and unknown patterns is fed into the trained encoder, and a distance-based recognition method is utilized to classify the jamming signals. The results demonstrate that the proposed model not only achieves sufficient learning and representation of known jamming patterns but also effectively identifies and classifies unknown jamming signals. When the jamming-to-noise ratio (JNR) exceeds 10 dB, the recognition rate for seven known jamming patterns and two unknown jamming patterns is more than 92%.

Multi-Technology Cooperative Driving: An Analysis Based on PLEXE

Cooperative Driving requires ultra-reliable communications, and it is now clear that no single technology will ever be able to satisfy such stringent requirements, if only because active jamming can kill (almost) any wireless technology. Cooperative driving with multiple communication technologies which complement each other opens new spaces for research and development, but also poses several challenges. The work we present tackles the fallback and recovery mechanisms that the longitudinal controlling system of a platoon of vehicles can implement as a distributed system with multiple communication interfaces. We present a protocol and procedure to correctly compute the safe transition between different controlling algorithms, down to autonomous (or manual) driving when no communication is possible. To empower the study, we also develop a new version of PLEXE, which is an integral part of this contribution as the only Open Source, free simulation tool that enables the study of such systems with a modular approach, and that we deem offers the community the possibility of boosting research in this field. The results we present demonstrate the feasibility of safe fallback, but also highlight that such complex systems require careful design choices, as naive approaches can lead to instabilities or even collisions, and that such design can only be done with appropriate in-silico experiments.

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.

Light‐weight deep learning method for active jamming recognition based on improved MobileViT

AbstractIn recent years, there has been significant research focused on identifying jamming signals using neural networks to improve identification accuracy. However, traditional networks require large‐scale datasets for training, which is challenging in small sample scenarios such as actual battlefield jamming signal data. Additionally, the large number of parameters in traditional models presents deployment challenges for devices. To address these issues, this study proposes a lightweight neural network model called MobileViT_CA, an improved version of MobileViT, for jamming recognition in small sample scenarios. MobileViT_CA adopts a coordinate attention mechanism suitable for lightweight networks, which further improves the classification performance of the network. The model converts one‐dimensional jamming signals into two‐dimensional time‐frequency distribution images through time‐frequency analysis and then uses the network for recognition. Compared with traditional neural network models, MobileViT_CA significantly reduces the network parameter volume while maintaining high recognition accuracy, accelerating network inference speed, and reducing hardware requirements. Experimental results show that MobileViT_CA performs well in small sample data. The model's fast learning ability has also been verified by using transfer learning, and the use of Grad‐CAM and t‐SNE to analyse MobileViT_CA demonstrates its advantages in feature recognition and extraction. Overall, this approach has the potential to significantly improve the reliability and effectiveness of jamming recognition systems in battlefield scenarios.

RDJCNN: A micro-convolutional neural network for radar active jamming signal classification

Timely awareness of jamming situations and classification of jamming categories are vital for radars to suppress jamming, ensure viability, and maintain functions in complex electromagnetic environments. To satisfy strict time requirements for radars on embedded devices, a micro-dynamic convolutional neural network for jamming signal classification is proposed in this paper. The proposed network takes the range-Doppler distribution obtained from built-in radar signal processing as input. The proposed data augmentation algorithm, together with the attention mechanism and the efficient convolutional architecture, improves the generalization capability and reduces the computational complexity. In addition, we propose a dynamic depth mechanism based on a task difficulty evaluator that enables the network to be adjusted automatically and further reduces the average computational complexity of classification. Simulation results verify the advantages of our approach in size, accuracy, and efficiency. The proposed network achieved 98.82% and 85.00% top-1 accuracy in two datasets with only 1.73 M multiply–accumulate operations.

Intelligent identification of radar active jamming type based on multi-domain information fusion

Feature fusion is beneficial to improving the recognition rate of radar active jamming types, which usually costs a lot of computation and network complexity, however. Contrapose the disadvantage of low computational power and resource allocation of missile-borne radar, this paper introduces data enhancement theory into the feature fusion method and proposes an intelligent identification model of radar active jamming type based on multi-domain information fusion. The model first analyzes the time-frequency domain features of the signal and one-dimensional features such as its real part, imaginary part, frequency spectrum, and power spectrum, and then uses the Cutout&Patchup algorithm to fuse the one-dimensional and two-dimensional features into a new multi-domain information fusion matrix as the input of the recognition network. The simulation results show that this method greatly improves the recognition accuracy of active jamming types. Under the WideResNet28_2 classifier, the classification accuracy of active interference type reaches 88.06%, which is 0.79% higher than that before fusion.

A Self-Supervised Method Based on CV-MUNet++ for Active Jamming Suppression in SAR Images

Synthetic aperture radar (SAR) system is susceptible to electromagnetic jamming during imaging, which seriously affects the subsequent interpretation of SAR images. Aiming at the problem of active suppressive jamming, this paper proposes a suppression method of SAR suppressive jamming based on self-supervised complex-valued deep learning, which consists of a novel complex-valued jamming suppression network CV-MUNet++ and a self-supervised training strategy. CV-MUNet++ could fully use the amplitude and phase information of complex-valued SAR images. The network’s weights, activation functions, and convolution operations are designed for complex domain processing. The different information representations of target and jamming in amplitude and phase in SAR images are mined to achieve jamming suppression. The self-supervised training strategy is proposed to solve the problem of relying heavily on manually labeled samples in the traditional network training process and is suitable for application scenarios where ground truth is difficult to obtain under complex jamming. The experimental results show that the proposed method could effectively suppress the active jamming of complex backgrounds and has the ability to self-supervised intelligent jamming suppression.

A Novel Countermeasure Method for Radar Coherent Jamming Based on Signal Feature Transformation

With the advancement of active jamming technology, a variety of new types of coherent jamming based on digital radio frequency memory (DFRM) 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 relevant countermeasures is necessary and challenging. In this paper, we take a fresh look at the problem and present a method for removing interference from received time-frequency overlapping signals based on signal feature modification (SFT). The antijamming problem is transformed into one linear underdetermined blind source separation (UBSS) model in a dual-channel receiving scenario using sparse component analysis, and the feasible solution is found by transforming the observed signal features to a new time-frequency domain representation. Simulations demonstrate that the developed technique outperforms standard interference countermeasures in terms of signal-to-interference ratio (SIR) and target identification performance, which are critical for antijamming.

Distinct impacts of polar and nematic self-propulsion on active unjamming

Though jamming transitions are long studied in condensed matter physics and granular systems, much less is known about active jamming (or unjamming), which commonly takes place in living materials. In this paper, we explore, by molecular dynamic simulations, the jamming-unjamming transition in a dense system of active semiflexible filaments. In particular, we characterize the distinct impact of polar vs nematic driving for different filament rigidities and at varying densities. Our results show that high densities of dynamic active filaments can be achieved by only changing the nature of the active force, nematic or polar. Interestingly, while polar driving is more effective at unjamming the system at high densities below confluency, we find that at even higher densities, nematic driving enhances unjamming compared to its polar counterpart. The effect of varying the rigidity of filaments is also significantly different in the two cases: While for nematic driving, lowering the bending rigidity unjams the system, we find an intriguing reentrant jamming-unjamming-jamming transition for polar driving as the filament rigidity is lowered. While the first transition (unjamming) is driven by softening due to reduced rigidity, the second transition (jamming) is a cooperative effect of ordering and coincides with the emergence of nematic order in the system. Together, through a generic model of self-propelled flexible filaments, our results demonstrate how tuning the nature of self-propulsion and flexibility can be employed by active materials to achieve high densities without getting jammed.

Intra-Pulse Frequency Coding Design for a High-Resolution Radar against Smart Noise Jamming

Smart noise jamming forms active jamming by intercepting, modulating, and forwarding radar signals into the radar receiver, which seriously affects the radar range recovery performance. In this paper, we propose a novel waveform design approach and an efficient range recovery method for high-resolution radar in the jamming scenario. Firstly, we propose an intra-pulse frequency-coded frequency-modulated continuous waveform (IPFC-FMCW), which contains multiple FMCW chips with different widths and frequencies, to combat the smart noise jamming. After the jamming suppression, the proposed waveform has a low sidelobe level, which is different from traditional FMCW signals for which the observations are periodically missing, resulting in high sidelobe levels. Then, to improve the range recovery performance of the waveform after jamming suppression, we optimize the range profile by designing the transmit waveform and then solve it by a simulated annealing algorithm. Next, based on the designed waveform, we derive the echo model after jamming suppression and propose a gridless compressed sensing (CS) method to recover the range of the targets. Compared with the existing waveforms and methods, the proposed waveform and the processing method achieve better range recovery performance in the jamming scenario. Numerical simulations are utilized to demonstrate the range recovery effectiveness of the proposed waveform and method in smart noise jamming.

Active jamming of microswimmers at a bottleneck constriction

A study of the light-induced evacuation of a suspension of microwimmers (the micro-alga Chlamydomonas reinhardtii) through a bottleneck-shaped constriction is presented. We show that a transition from a jammed phase to an uninterrupted phase occurs when varying either the door size or the swimming velocity. The survival function of exit times is then found to evolve from a power law to an exponential trend. Moreover, we found that the evacuation time increases with increasing velocity, a behavior reminiscent of the ``faster-is-slower'' paradox present in crowd dynamics systems.

Interplay between jamming and motility-induced phase separation in persistent self-propelling particles.

In living and engineered systems of active particles, self-propulsion induces an unjamming transition from a solid to a fluid phase and phase separation between a gas and a liquidlike phase. We demonstrate an interplay between these two nonequilibrium transitions in systems of persistent active particles. The coexistence and jamming lines in the activity-density plane meet at the jamming transition point in the limit of hard particles or zero activity. This interplay induces an anomalous dynamic in the liquid phase and hysteresis at the active jamming transition.

Recognition of Active Deception Jamming for LFM Radar Based on Hyperdimensional Computing

Active deception jamming is one of the common means to jam radar signals. How to effectively recognize active deception jamming is a challenge of modern radar technology. To address the accuracy and real-time of radar active jamming recognition in practical applications, we propose a fast recognition method of radar active deception jamming based on hyperdimensional computing. First, to address the high feature dimensions, we use the sparse representation of time–frequency diagrams as the input, which time–frequency diagrams are constructed by the jamming plus echo signals in multiple consecutive pulse periods. Second, to achieve a high accuracy and real time of recognition, we employ the hyperdimensional computing to map the input to hyperdimensional space for training and recognition. Last, in the recognition stage, the Hamming distance is adopted to measure the similarity of samples. We validate our method on nine types of radar active deception jamming. The experimental results show that compared with the existing mainstream machine learning-based methods, the proposed method greatly shortens the training time on the premise of maintaining a high recognition accuracy.