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The integrated communication and jamming system is proposed by exploiting the diversity gain and flexible deployment of unmanned aerial vehicle (UAV)‐based distributed multiple input multiple output technology, which is expected to gain higher spectral efficiency and system scalability. This paper designs transmit beamforming of multiple UAVs so that they can simultaneously impose jamming signal to jamming targets and communication signal to communication users at the same frequency. The aim is to maximize the total jamming effect of all jamming targets while achieving a certain communication rate of communication users by optimizing the transmit beamforming matrices. Two cases are considered in which the UAV swarm works in clock synchronization and clock asynchronization. Specifically, the problem is formulated into a non‐convex optimization in the synchronous case. A near optimal solution is derived by decoupling the beamforming matrix into multi‐user interference suppression matrix and single‐user beamforming matrix. In the asynchronous case, the transmitter–receiver matching relation needs to consider and multiple optimization variables are highly coupled, penalty factors and intermediate variables to convert the optimization problem to convex programming are introduced. The simulation results show that the proposed beamforming design algorithm coordinates the communication and jamming signal rationally and outperforms the conventional beamforming design algorithms. [ABSTRACT FROM AUTHOR]

This study examines the increasing manipulation of the Automatic Identification System (AIS) and its impact on maritime safety. By analyzing instances of AIS disruptions, the study identifies vulnerabilities and risks associated with AIS disruptions. The study also examines the legal framework regarding regulatory response to AIS-related problems. Technological advances and best practices to improve the reliability and security of AIS are proposed. The importance of international cooperation in combating AIS manipulation and ensuring maritime safety is emphasized.

Abstract Precise radar pulse detection is the premise of electronic support measures. A constant false alarm rate (CFAR) detection algorithm based on difference of box (DOB) filter is proposed to realise low‐complexity and environment‐adaptive radar pulse detection, and the principle behind the proposed algorithm is revealed. Specifically, the DOB filter is adopted to extract the rising edges and falling edges of radar pulses, and the signal variation law of probability density functions (PDFs) during the process of DOB filter are first theoretically analysed. Based on the derived PDFs, dynamic detection threshold and fixed threshold factor for the proposed CFAR algorithm based on DOB filter are deduced to detect the presence of radar pulses. Furthermore, the closed‐form expression of the detection probability is derived. Simulations and on‐board field programmable gate arrays implementation verify the superiority of the proposed CFAR algorithm based on DOB filter. Simulation results show that the proposed algorithm performs well in detecting various types of pulse signals, the detection probability ≥99% on condition that the signal‐to‐noise ratio ≥3 dB and false alarm probability = 10−8. Moreover, the proposed CFAR algorithm based on DOB filter can deal with both singular pulses and pulse on pulse signals.

Abstract In the increasingly intricate electromagnetic environment, the radar receiver may simultaneously encounter multiple intentional or unintentional jamming signals, which results in temporal and spectral overlap of received signals and forms a composite jamming signal. The nature and extent of interference contained in the received signal are often unknown, while they significantly affect the accuracy of radar detection. AnOpen‐Set Compound Jamming Signal Recognition Framework based on Multi‐Task Multi‐Label (MTML‐OCJR) is proposed. Based on the time–frequency characteristic of compound jamming signals, the proposed framework employs multi‐label classification to identify components of compound jamming signals while incorporating an unknown signal detection task into the classification process. Time–frequency image reconstruction combined with extreme value model estimation is used to detect unknown types of jamming signals, enabling simultaneous signal recognition and anomaly detection. The obtained results show that the proposed approach has superior recognition performance for composite jamming signals in closed‐set environments and high anomaly detection ability for unknown signals in open‐set environments. This method has the potential to significantly enhance the effectiveness and reliability of jamming systems in battlefield scenarios.

It is shown that focusing systems built on the basis of a system of radio receivers spaced apart in space and simulating the operation of a lens can be used to ensure the protection of information during data transmission in the direct radio visibility zone. The synthesis of the lens equivalent in this case is carried out by using a system of phase shifters that change the phase of the oscillations arriving at each of the receivers, so that the receiving system is tuned to a radio wave source located at a specific point in space. In this case, information protection is provided according to the “friend or foe” principle, and any commands coming from other areas of space, except for the point where the authorized operator is located, are ignored. The advantage of this approach is the ability to partially or completely abandon the use of cryptographic methods. It is shown that the proposed approach is of considerable interest for ensuring the stable operation of groups of unmanned aerial vehicles from the point of view of the possibility of intercepting control using electronic warfare methods.

Abstract Robustness against Electronic Warfare/Electronic Defence attacks represents an important advantage of Noise Radar Technology (NRT). An evaluation of the related Low Probability of Detection (LPD) and of Intercept (LPI) is presented for Continuous Emission Noise Radar (CE‐NR) waveforms with different operational parameters, that is, “tailored”, and with various “degrees of randomness”. In this frame, three different noise radar waveforms, a phase Noise (APCN) and two “tailored” noise waveforms (FMeth and COSPAR), are compared by time–frequency analysis. Using a correlator (i.e. a two antennas) receiver, assuming a complete knowledge of the band (B) and duration (T) of the coherent emission of these waveforms, it will be shown that the LPD features of a CE‐NR do not significantly differ from those of any CE radar transmitting deterministic waveforms. However, in real operations, B and T are unknown; hence, assuming an instantaneous bandwidth estimation will show that the duration T can be estimated only for some specific “tailored” waveforms (of course, not to be operationally used). The effect of “tailoring” is analysed with prospects for future work. Finally, some limitations in the classification of these radar signals are analysed.

Abstract In electronic warfare, radar signal deinterleaving is a critical task. While many researchers have applied deep learning and utilised known radar classes to construct interleaved pulse sequences training sets for deinterleaving models, these models face challenges in distinguishing between known and unknown radar classes in open‐set scenarios. To address this challenge, the authors propose a novel model, the Reconstruction Bidirectional Recurrent Neural Network (RBi‐RNN). RBi‐RNN utilises input reconstruction and employs a joint training strategy incorporating cross‐entropy loss, reconstruction loss, and centre loss. These strategies aim to maximise inter‐class latent representation distances while minimising intra‐class disparities. By incorporating an open‐set recognition method based on extreme value theory, RBi‐RNN adapts to open‐set scenarios. Simulation results demonstrate the superiority of RBi‐RNN over conventional models in both closed‐set and open‐set scenarios. In open‐set scenarios, it successfully discriminates between known and unknown radar signals within interleaved pulse sequences, deinterleaving known radar classes with high stability. The authors lay the foundation for future unsupervised deinterleaving methods designed specifically for unknown radar pulses.

This paper presents a miniaturized Vivaldi antenna with a scan volume of ± 30° designed for the 6–18 GHz tactical operational band, commonly employed in phased array systems and modern surveillance applications. The proposed antenna offers enhanced radiation characteristics, low cross polarization, broad beam width, and moderate gain. The design presented in this paper follows a step-by-step approach to achieve enhanced performance in terms of multi-octave bandwidth and compact size. Initially, a traditional Vivaldi antenna was designed and examined, with a modified feed structure. Subsequently, corrugated edges were inserted into the antenna, increasing its electrical length and resulting in good impedance matching, especially in the 6–10 GHz frequency band. A prototype model is fabricated and tested to validate its performance against the simulation results. The measured results demonstrate that the return loss (S11) remains below −10 dB across the operating frequency range, achieving a 50% bandwidth, with a gain of 3–7 dB, and improved co-cross polarization of −23 dB at 6 GHz and −26 dB at 18 GHz. [ABSTRACT FROM AUTHOR]

Precise radar pulse detection is the premise of electronic support measures. A constant false alarm rate (CFAR) detection algorithm based on difference of box (DOB) filter is proposed to realise low‐complexity and environment‐adaptive radar pulse detection, and the principle behind the proposed algorithm is revealed. Specifically, the DOB filter is adopted to extract the rising edges and falling edges of radar pulses, and the signal variation law of probability density functions (PDFs) during the process of DOB filter are first theoretically analysed. Based on the derived PDFs, dynamic detection threshold and fixed threshold factor for the proposed CFAR algorithm based on DOB filter are deduced to detect the presence of radar pulses. Furthermore, the closed‐form expression of the detection probability is derived. Simulations and on‐board field programmable gate arrays implementation verify the superiority of the proposed CFAR algorithm based on DOB filter. Simulation results show that the proposed algorithm performs well in detecting various types of pulse signals, the detection probability ≥99% on condition that the signal‐to‐noise ratio ≥3 dB and false alarm probability = 10−8. Moreover, the proposed CFAR algorithm based on DOB filter can deal with both singular pulses and pulse on pulse signals. [ABSTRACT FROM AUTHOR]

In the increasingly intricate electromagnetic environment, the radar receiver may simultaneously encounter multiple intentional or unintentional jamming signals, which results in temporal and spectral overlap of received signals and forms a composite jamming signal. The nature and extent of interference contained in the received signal are often unknown, while they significantly affect the accuracy of radar detection. AnOpen‐Set Compound Jamming Signal Recognition Framework based on Multi‐Task Multi‐Label (MTML‐OCJR) is proposed. Based on the time–frequency characteristic of compound jamming signals, the proposed framework employs multi‐label classification to identify components of compound jamming signals while incorporating an unknown signal detection task into the classification process. Time–frequency image reconstruction combined with extreme value model estimation is used to detect unknown types of jamming signals, enabling simultaneous signal recognition and anomaly detection. The obtained results show that the proposed approach has superior recognition performance for composite jamming signals in closed‐set environments and high anomaly detection ability for unknown signals in open‐set environments. This method has the potential to significantly enhance the effectiveness and reliability of jamming systems in battlefield scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a study of the impact of different types of waveform jamming schemes against remote detonation communication devices based on Long term evolution (LTE) technology. Two main categories of waveform jamming signals (single-carrier and multi-carriers) were used. Also, two jamming scenarios against LTE-based remote detonation devices (jamming the whole LTE frame and jamming the LTE synchronization channel) were investigated. The key point of evaluating the effectiveness of the presented waveform jamming signals is by measuring their ability to successfully cut off the connection between the transmitter and the LTE-based remote detonation device. This is achieved by verifying that the remote detonation device is unable to successfully receive the transmitted information (Deny of Service (DOS) mode). To this end, emulations of these scenarios are held using two Keysight software-defined radio software programs (SystemVue 2015 and VSA 89600). Finally, a real practical experiment of the impact of different waveform jamming schemes against LTE-based remote detonation devices is emulated to present the optimal waveform jamming scheme against these types of devices. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Robustness against Electronic Warfare/Electronic Defence attacks represents an important advantage of Noise Radar Technology (NRT). An evaluation of the related Low Probability of Detection (LPD) and of Intercept (LPI) is presented for Continuous Emission Noise Radar (CE‐NR) waveforms with different operational parameters, that is, "tailored", and with various "degrees of randomness". In this frame, three different noise radar waveforms, a phase Noise (APCN) and two "tailored" noise waveforms (FMeth and COSPAR), are compared by time–frequency analysis. Using a correlator (i.e. a two antennas) receiver, assuming a complete knowledge of the band (B) and duration (T) of the coherent emission of these waveforms, it will be shown that the LPD features of a CE‐NR do not significantly differ from those of any CE radar transmitting deterministic waveforms. However, in real operations, B and T are unknown; hence, assuming an instantaneous bandwidth estimation will show that the duration T can be estimated only for some specific "tailored" waveforms (of course, not to be operationally used). The effect of "tailoring" is analysed with prospects for future work. Finally, some limitations in the classification of these radar signals are analysed. [ABSTRACT FROM AUTHOR]

The role of unmanned aerial vehicles (UAVs) is becoming increasingly important for military conflicts. UAVs are more and more strongly considered as a force multiplier that could play a decisive role for the final outcome of an ambiguous battle. While the role of UAVs for Identification/Surveillance/Reconnaissance missions has been well established within many modern armies, their contribution to the upgrade or the restoration of military communications is still vague. In the current paper we review the current situation in military communications utilizing UAV platforms, and we also explore its future trends and capabilities taking into consideration the parallel developments in the civil UAV manufacturing sector involving UAV swarms and fifth-generation networks. Furthermore, a set of realistic combat scenarios involving the utilization of UAVs as a multipurpose communication node is presented and discussed, highlighting its critical role as a catalyst for the successful accomplishment of diverse military missions. [ABSTRACT FROM AUTHOR]

The arrival of the nuclear age highlighted the inadequacy of U.S. and allied information networks for transmitting vital messages across the globe. Submarine cables could be cut and radio easily jammed. U.S. and allied officials looked to emerging space technologies to compensate for the inadequacies of radio and submarine cables and to reduce their dependence on terrestrial telecommunications hardware. But satellites were also vulnerable and insufficient for meeting all communications requirements. Consequently, creating layered telecommunications networks that stretched from under the sea, across the land, and into space emerged as the optimal strategy for strengthening information security. [ABSTRACT FROM AUTHOR]

With the development of electronic warfare technology, the intelligent jammer dramatically reduces the performance of traditional radar anti-jamming methods. A key issue is how to actively adapt radar to complex electromagnetic environments and design anti-jamming strategies to deal with intelligent jammers. The space of the electromagnetic environment is dynamically changing, and the transmitting power of the jammer and frequency diversity array (FDA) radar in each frequency band is continuously adjustable. Both can learn the optimal strategy by interacting with the electromagnetic environment. Considering that the competition between the FDA radar and the jammer is a confrontation process of two agents, we find the optimal power allocation strategy for both sides by using the multi-agent deep deterministic policy gradient (MADDPG) algorithm based on multi-agent reinforcement learning (MARL). Finally, the simulation results show that the power allocation strategy of the FDA radar and the jammer can converge and effectively improve the performance of the FDA radar and the jammer in the intelligent countermeasure environment. [ABSTRACT FROM AUTHOR]

With the maturation of digital radio frequency memory (DRFM) technology, various intra-pulse retransmission interference methods have emerged. These flexible and changeable retransmission interference methods pose significant challenges to radar detection tasks, particularly in modern battlefields. This paper proposes an attention-guided complex-valued transformer (AGCT) as a solution. First, the encoder maps the received signal contaminated by interference and noise into a high-dimensional space. Then, the dilated convolution block (DCB) group and attention block (AB) group in the mask estimator extract the delicate multi-scale features and large-scale features of the interference, respectively, to obtain a multidimensional space mask. Finally, the decoder restores interference to the time domain and outputs the estimated target echo using residual learning. Considering the characteristics of intra-pulse interference, we introduced the energy attention block (EAB) at the end of the DCBs and the ABs within our network. This addition ensures a heightened focus on extracting interference features. Furthermore, we implemented a curriculum learning strategy during the network training. This approach gradually acclimates the network to fit different types of retransmission interference, starting from simpler to more complex scenarios. Our extensive experiments, conducted under various conditions, have provided compelling evidence of the AGCT's superior performance. Compared to the comparative network, the AGCT's advantages are particularly pronounced under more harsh conditions, demonstrating its robustness and effectiveness. [ABSTRACT FROM AUTHOR]

In electronic warfare, radar signal deinterleaving is a critical task. While many researchers have applied deep learning and utilised known radar classes to construct interleaved pulse sequences training sets for deinterleaving models, these models face challenges in distinguishing between known and unknown radar classes in open‐set scenarios. To address this challenge, the authors propose a novel model, the Reconstruction Bidirectional Recurrent Neural Network (RBi‐RNN). RBi‐RNN utilises input reconstruction and employs a joint training strategy incorporating cross‐entropy loss, reconstruction loss, and centre loss. These strategies aim to maximise inter‐class latent representation distances while minimising intra‐class disparities. By incorporating an open‐set recognition method based on extreme value theory, RBi‐RNN adapts to open‐set scenarios. Simulation results demonstrate the superiority of RBi‐RNN over conventional models in both closed‐set and open‐set scenarios. In open‐set scenarios, it successfully discriminates between known and unknown radar signals within interleaved pulse sequences, deinterleaving known radar classes with high stability. The authors lay the foundation for future unsupervised deinterleaving methods designed specifically for unknown radar pulses. [ABSTRACT FROM AUTHOR]

Abstract The integrated communication and jamming system is proposed by exploiting the diversity gain and flexible deployment of unmanned aerial vehicle (UAV)‐based distributed multiple input multiple output technology, which is expected to gain higher spectral efficiency and system scalability. This paper designs transmit beamforming of multiple UAVs so that they can simultaneously impose jamming signal to jamming targets and communication signal to communication users at the same frequency. The aim is to maximize the total jamming effect of all jamming targets while achieving a certain communication rate of communication users by optimizing the transmit beamforming matrices. Two cases are considered in which the UAV swarm works in clock synchronization and clock asynchronization. Specifically, the problem is formulated into a non‐convex optimization in the synchronous case. A near optimal solution is derived by decoupling the beamforming matrix into multi‐user interference suppression matrix and single‐user beamforming matrix. In the asynchronous case, the transmitter–receiver matching relation needs to consider and multiple optimization variables are highly coupled, penalty factors and intermediate variables to convert the optimization problem to convex programming are introduced. The simulation results show that the proposed beamforming design algorithm coordinates the communication and jamming signal rationally and outperforms the conventional beamforming design algorithms.

Abstract 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.

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. [ABSTRACT FROM AUTHOR]

Multi-Function Radars (MFRs) are sophisticated sensors with great agility and flexibility in adapting their transmitted waveform and control parameters. The recognition of MFR work modes based on the intercepted pulse sequences plays an important role in interpreting the functional purpose and threats of a non-cooperative MFRs. However, due to the increased flexibility of MFRs, radar work modes with emerging new modulations and control parameters always appear, and the supervised classification method suffers performance degradation or even failure. Unsupervised learning and clustering of MFR pulse sequences becomes urgent and important. This paper establishes a unified multivariate MFR time series feature extraction and clustering framework for MFR work mode recognition. At first, various features are collected to form the feature set. The feature set includes features extracted through deep learning based on recurrent auto-encoders, multidimensional time series toolkit features, and manually crafted features for radar inter-pulse modulations. Subsequently, several feature selection algorithms, combined with different clustering and classification methods, are used for the selection of an "optimal" feature subset. Finally, the effectiveness and superiority of the proposed framework and selected features are validated through simulated and measured datasets. In the simulated dataset containing 20 classes of work modes, under the most severe non-ideal conditions, we achieve a clustering purity of 73.46% and an NMI of 84.28%. In the measured dataset with seven classes of work modes, we achieve a clustering purity of 86.96% and an NMI of 90.10%. [ABSTRACT FROM AUTHOR]

Countries in Europe have experienced radio frequency interference from Russian electronic warfare units, affecting navigation systems without discrimination. Interference has been identified as coming from the Russian mainland, Kaliningrad, and in the areas where it is engaged in conflicts abroad, creating serious hazards for aircraft. Previous research has identified the technical vulnerabilities to aviation from radio frequency interference, but it has yet to be contextualised in light of the ongoing geopolitical tensions. Using literature review analysis, the aim of the article is to place the jamming and spoofing threat in the context of ongoing political tensions between Russia and the region and to establish worse-case scenarios based on the former’s motives. Focusing on the threats to aircraft, it finds that the likely motivations are to (1) complement political narratives of Western aggression; (2) to deny service for intimidation, harassment, economic loss, and to portray a dominant Russian cyber influence; and (3) to use spoofing to enable hostage diplomacy via the seizure of people and assets. It argues that reporting mechanisms for aviation risks assumes geographical staticism, which does not correspond adequately to the threat. It also creates arbitrary “predictability” in otherwise unpredictable environments, which could ultimately affect vigilance and due diligence in the areas not considered problematic. [ABSTRACT FROM AUTHOR]

Supremacy in the electromagnetic spectrum provides additional options and increased flexibility in any competition, even other than military conflicts, in addition to greater control of the scale of escalation. It gives policy makers and military leaders the decision space to manage events in real time while denying equivalent decision space to adversaries. At the same time, supremacy in the electromagnetic spectrum requires the collection, analysis and validation of robust information in the following key areas: electromagnetic spectrum sensors, communications, data links, radars, jamming, directed energy and infrared systems, engineering data describing the performance, characteristics and signature information of associated equipment, weapons and platforms, combat support data and modelling and simulation support. The authors describe these topics through a detailed analysis of the importance and role of supremacy in the electromagnetic spectrum. [ABSTRACT FROM AUTHOR]

Supremacy in the electromagnetic spectrum provides additional options and increased flexibility in any competition, even other than military conflicts, in addition to greater control of the scale of escalation. It gives policy makers and military leaders the decision space to manage events in real time while denying equivalent decision space to adversaries. At the same time, supremacy in the electromagnetic spectrum requires the collection, analysis and validation of robust information in the following key areas: electromagnetic spectrum sensors, communications, data links, radars, jamming, directed energy and infrared systems, engineering data describing the performance, characteristics and signature information of associated equipment, weapons and platforms, combat support data and modelling and simulation support. The authors describe these topics through a detailed analysis of the importance and role of supremacy in the electromagnetic spectrum. [ABSTRACT FROM AUTHOR]

State-of-the-art distributed system development with low power specifications is especially required for different applications. Ultra-wideband radar (UWB), Multi Input Multi Output radar (MIMO), and cognitive-based radar technology are the current trends in modern radar systems. Current EW systems are developed with digital receiver technology using Nyquist Sampling. Radar modulation recognition and correct radar identification are essential requirements for EW receiver systems. The radar signal acquisition through compressed sensing and reducing the number of samples using sub-Nyquist sampling play a vital role in EW systems. Random Modulator preintegrator (RMPI) is a current concept for extracting waveforms with dominant base vectors. RMPI explores the structural and geometrical properties of the signal apart from traditional time and frequency domain analysis for improved identification. The Curvelet transform-based RMPI methodology is an inverse problem with exceptionally fewer measurements. In this paper, Curveletbased modulation estimation, identification of radars, and Curvelet-simulated results of different radar waveforms with noise are depicted. The discrete Curvelet transform algorithm (USFFT) is simulated with an input signal of Ultra-Wide Band (UWB) pulse, pulse spikes, Linear Frequency Modulated (LFM), and Barker-coded pulse waveforms. The FFT output is also measured with Nyquist samples for comparison. This paper has contributed the simulation of Electronic Warfare techniques with Modulation Estimations as part of ESM (Electronic Support Measure), the Compressed sensing and Curvelet-based modulation estimation, identification of radars simulated with different radar waveforms with/without noise, and the discrete Curvelet transform algorithm is simulated with input signal of UWB pulse, pulse spikes, LFM, and Barker coded. [ABSTRACT FROM AUTHOR]

This paper presents a signal generation architecture for simulating arbitrarily intercepted low-probability-of-intercept (LPI) signals in a battlefield environment. Additionally, the performances of deep learning models in classifying the generated signals are compared in terms of the classification time, GPU memory usage, and accuracy. Previous studies have utilized ensemble learning to minimize signal classification time and enhance accuracy; however, an explicit criteria for the adoption of deep learning models have been lacking. This paper presents the analysis of the performances of 11 deep learning models on the basis of simulation results and proposes an ensemble model that utilizes MobileNet V3 Small as the main model and Densenet-169 as the sub-model. [ABSTRACT FROM AUTHOR]

To protect our receivers against spoofing, intrusion or simply reduce interference, we proposed a multi-layered architecture which can be integrated into a receiver chain by modifying the analog and digital parts. Indeed, Our project addresses analog circuits coupled with digital approaches to immunize a radio against any intentional electromagnetic attack (intelligent jammer or not), or radio interference. In this manuscript, we only consider the innovative analog part of our system which is based on non-linear bijective functions generated by electronic circuits. In fact, we have developed an electronic system characterized by a non-linear bijective function that can be implemented using analog electronic components, and its inverse function can be implemented in the digital part of our reception chain. The main purpose of these two functions is the protection of an interesting weak signal existing in the vicinity to an annoying but very strong signal which will risk saturating the Low Noise Amplifier (LNA), and therefore the target signal can no more be chased after the analog to digital convertor (ADC). Certainly, in conventional transmission systems, we generally seek to eliminate or to reduce the impact of non-linear effects of certain electronic components or devices; however, our proposed scheme introduce a specific non-linear function in ordre to detect the presence of signals with weak Signal to Interference plus Noise Ratios (SINR). This manuscript presents our study and discusses various aspects related to the proposed new nonlinear scheme.

Fourth-generation (4G) mobile networks are successively replaced by fifth-generation (5G) ones, based on the new releases of the 3rd Generation Partnership Project (3GPP) standard. 5G generation is dedicated to civilian users and the conducted analytical work shows that it has numerous technological gaps that prevent its direct implementation in military communications systems. However, the recent armed world conflicts showed that closed or public mobile networks are willingly used by soldiers for both private and business communications, and to conduct defensive and offensive operations as well. From the military operation viewpoint, jamming both civil and military systems is one of the essential elements of electronic warfare. This paper focuses on the practical trial of low-energy and smart jamming on a 5G private network using narrowband signals, which facilitates the reduction of the available throughput, e.g. in the time division duplex - uplink (TDD-UL) by 99%, or by 82% in the frequency division duplex - downlink (FDD-DL). This type of jamming also allows for reaching up to 25 dB of energy gain comparing to barrage jamming. The authors moreover investigated jamming the Narrowband IoT radio interface using synchronized, selective jamming. The goal was to propose energy efficient methods that will allow the jammers to work longer and be mounted on a small unmanned aerial vehicle (UAV) that can operate near the gNB. The generation of low-power jamming signals in the gNB vicinity successfully hinders detecting the jammer by the enemy’s electronic reconnaissance systems. The proposed solutions are compared with the test results for other types of jamming methods.

This article is a guest editorial that discusses advancements and future trends in noise radar technology. Noise radar technology uses random and non-periodic radar signals to eliminate ambiguities present in other radars. The article explores the practical aspects of noise radar technology, such as evaluating the degree of randomness in transmission and the computational load of signal processing. It also covers topics like electronic warfare, anti-intercept features, and the potential applications of artificial intelligence in noise radar technology. The article includes specific papers on implementation aspects, waveform selection, anti-intercept features, and artificial intelligence applications in noise radar technology. The authors hope that this special issue provides valuable insights into the advancements and future trends of noise radar technology. [Extracted from the article]

Who could have guessed 120 years ago, when electronic warfare (EW) was accidentally discovered, that it would have such a big impact on battlefield? For almost 30 years, after the cold war ended, electronic warfare was forgotten, but the war from Ukraine, revived it again. What’s in an electronic warfare system? It is an integrated, autonomous, portable or vehicle mounted electronic system, whether configured to attack, protect or support, must have a way to collect and make sense of the signals in its environment. Electronic warfare systems can be configured for a variety of different missions and use a lot of different subsystems. It must identify what’s out there, understand how it is using the spectrum, and determining if it is a threat. The system must then neutralize it and passes the threat data to the technique generator which determines how the system should respond to address the threat. For an electronic system to conduct electronic attack or electronic protect missions, it must be able to broadcast signals of its own to dominate the electromagnetic spectrum. Once a threat is analyzed, and a response generated, it is the ability of the EW system’s transmitters to precisely radiate electromagnetic energy that makes jamming, spoofing, deception and other electronic activities. [ABSTRACT FROM AUTHOR]

With the modern development of information technology, remote control devices are becoming increasingly widespread. Their proliferation is primarily due to their main advantage - the ability to conduct observations or perform certain operations in places that are dangerous or completely unsuitable for human presence. One such application is the use of unmanned devices during military operations, which allows the operator to be in relative safety. Such use of unmanned systems, in turn, encourages the creation and development of methods to counter these systems, disabling communication and control channels, or detecting and locating the operator’s location. For this, there is a wide range of electronic intelligence tools, electronic warfare, direction finding stations, etc. The object of this study is the task of increasing the level of protection of unmanned devices and complexes against enemy electronic warfare means and the task of restoring communication and functionality of the device in case of failures in its operation due to accidental obstacles or actions of the attacker. The article contains a brief overview of modern methods and means used to ensure the protection of radio transmissions and analyzes the possibility of their application in small unmanned aviation systems, which are now most used by the military at the current stage of confrontation with the enemy. Based on this, a method of implementing communication protection measures to restore the functionality of the unmanned device was proposed, the principle of its operation is described. The main relevant problem underlying the study is the insufficient protection of communication and navigation systems in civilian solutions and electronic devices that are now actively used to build unmanned vehicles used in military and reconnaissance operations. The obtained results will allow to increase the protection of unmanned systems by applying frequency retuning means to improve the stability and reliability of communication in UAV control systems. [ABSTRACT FROM AUTHOR]

Wireless communication technologies are undergoing intensive study and are experiencing accelerated progress which leads to a large increase in the number of end-users. Because of this, the radio spectrum has become more crowded than ever. These previously mentioned aspects lead to the urgent need for more reliable and intelligent communication systems that can improve the spectrum efficiency. Specifically, modulation scheme recognition occupies a crucial position in the civil and military application, especially with the emergence of Software Defined Radio (SDR). The modulation recognition is an indispensable task while performing spectrum sensing in Cognitive Radio (CR). Spread spectrum (SS) techniques represent the foundation for the design of Cognitive Radio systems. In this work, we propose a new method of characterization of Spread spectrum modulations capable of providing relevant information for the process of recognition of this type of modulations. Using the proposed approach, results higher than 90% are obtained in the modulation classification process, thus bringing an advantage over the classical methods, whose performance is below 75%. [ABSTRACT FROM AUTHOR]

The Russia-Ukraine War holds many lessons for the US Army and American policymakers and leaders on the nature and role of reconnaissance-strike complexes in modern combat, especially Ukraine's development of a battle-management system that fuses unmanned aeria l systems and satellite reconnaissance to enable the fire coordination for deep strikes into the enemy rear. In the research presented here, open-source analysis and interviews in Ukraine focus on the development and employment of reconnaissance-strike complexes with respect to deep strike and the likelihood of mutual territorial attack. [ABSTRACT FROM AUTHOR]

Abstract The integration of electronic reconnaissance platforms and payloads in the electromagnetic and spatial domains of cognition is necessary for the successful execution of cognitive electronic reconnaissance missions. However, in partially observable environments, platforms and payloads interact in separate operational domains, making it a challenge for the model. The authors create a theoretical model of cognitive electronic reconnaissance, present an analysis method of model elements and derives adequate requirements for optimal radiation source sorting or recognition. Then, a 3D cognitive reconnaissance simulator ‘Scouter’ to simulate the dynamic interaction process of space electronic reconnaissance missions is developed and a deep reinforcement learning framework to optimise the electronic reconnaissance strategies of Unmanned Aerial Vehicle (UAV) is designed. For the first time, a method for judging reconnaissance area coverage using a behavioural angle is proposed. This method enables a deep reinforcement learning‐based UAV with better reconnaissance behaviours and to realise the stable targeting of the target by the receiving antenna beam during reconnaissance. Moreover, the fast convergence of UAV agent training is ensured through the normalisation of partially observable status and reward shaping. Verified by adequate numerical simulations, a trained UAV can complete a 100 reconnaissance cycles mission with a completion rate of 91.9%. Even after the reconnaissance cycles are prolonged to 600, the mission completion rate can still be maintained at 78.5%. The most satisfying aspect is that the UAV comprehends the idea of reconnaissance and behaves in a way that is typical of it, such as by reciprocating or circling the edge of the radar's detecting power.

The article is devoted to the development of a method for increasing the efficiency of communication channels of unmanned aerial vehicles (UAVs) in the conditions of electronic warfare (EW). The author analyses the threats that may be caused by the use of electronic warfare against autonomous UAVs. A review of some technologies that can be used to create original algorithms for countering electronic warfare and increasing the autonomy of UAVs on the battlefield is carried out. The structure of modern digital communication systems is considered. The requirements of unmanned aerial vehicle manufacturers for onboard electronic equipment are analyzed, and the choice of the hardware platform of the target radio system is justified. The main idea and novelty of the proposed method are highlighted. The creation of a model of a cognitive radio channel for UAVs is considered step by step. The main steps of modelling the spectral activity of electronic warfare equipment are proposed. The main criteria for choosing a free spectral range are determined. The type of neural network for use in the target cognitive radio system is substantiated. The idea of applying adaptive coding in UAV communication channels using multicomponent turbo codes in combination with neural networks, which are simultaneously used for cognitive radio, has been further developed.

Electronic warfare (Polish acronym WRE ‒ walka radioelektroniczna) is a set of militaryactions based on the use of the electromagnetic spectrum and it includes:— reception and identification of electromagnetic emissions,— reducing the effectiveness of the enemy’s electronic devices,— enabling effective use of the spectrum by own forces.The task of the WRE in peacetime is to obtain information about radio-electronic devices, especiallyradars. These tasks are performed by ELINT (Electronic Intelligence) and/or ESM (Electronic WarfareSupport Measures) devices. They operate passively in the 0.5÷18 GHz band, their detection is difficultand the information they provide allows to detect a threat to own forces and to take appropriatecountermeasures. Radar recognition is a complex process that involves the following stages. The firststage: gathering measurements and their grouping into the so-called packets and graphical display ofthe received pulse trains. The measurements are represented by the so-called measurement vectors(descriptors) containing signal parameters, including the TOA (Time of Arrival) pulse with nanosecondaccuracy. The second stage: associating pulse packets with logical sources in order to obtainrepresentative data strings. This stage is signal sorting and deinterleaving. The third stage: estimationof the WS signal vector based on the associated pulse packets. The final stage of data processing is thecomparison of the obtained signal descriptor parameters WS with Database (BD) radar patterns. Thereconnaissance result allows for the assessment of the threat resulting from the operation of this radar.Radar recognition requires precise determination of many parameters of its signal. For pulsed radars,important parameters are the Pulse Repetition Interval (PRI) and the type of PRI modulation. Radarsare characterised by a high complexity of PRI changes. This provides great utility and makes it possibleto distinguish even radar units and changes in their locations. This allows for the detection and fullidentification of objects that use radars (e.g. ships) with the given object completely out of sight; it alsoallows to predict intentions of the target. The basic inter-pulse PRI modulations include: stagger, dwelland switch, jitter or sliding. PRI changes in radar signals may be periodic with long cycles, difficultto identify in cluttered conditions. Unfortunately, the received pulse trains are usually distorted. Thismakes the WS estimation process difficult ‒ especially in automatic mode. PRI analysis has receivedmuch attention in the literature. The methods are computationally complex and have numerouslimitations. This article presents ASWC algorithm (Sequential Cycle Detection Algorithm) of PRIproprietary, with relatively low computational complexity. Examples of test results confirming its higheffectiveness in automatic PRI analysis performed under interference conditions were also presented.Keywords: radars, electronic warfare, radar recognition, ELINT, ESM, pulse descriptor, PRI

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.

With the development of electronic warfare technology, the intelligent jammer dramatically reduces the performance of traditional radar anti-jamming methods. A key issue is how to actively adapt radar to complex electromagnetic environments and design anti-jamming strategies to deal with intelligent jammers. The space of the electromagnetic environment is dynamically changing, and the transmitting power of the jammer and frequency diversity array (FDA) radar in each frequency band is continuously adjustable. Both can learn the optimal strategy by interacting with the electromagnetic environment. Considering that the competition between the FDA radar and the jammer is a confrontation process of two agents, we find the optimal power allocation strategy for both sides by using the multi-agent deep deterministic policy gradient (MADDPG) algorithm based on multi-agent reinforcement learning (MARL). Finally, the simulation results show that the power allocation strategy of the FDA radar and the jammer can converge and effectively improve the performance of the FDA radar and the jammer in the intelligent countermeasure environment.