Jamming Scheme Research Articles

Design of anti‐jamming decision‐making for cognitive radar

AbstractWith the development of electronic warfare, anti‐jamming measure becomes more and more complex. There have been certain research results on jamming strategies, but only a few research materials on anti‐jamming strategies. It is difficult to simulate the real jamming environment, and there is no appropriate anti‐jamming decision‐making model for research. Cognitive radar can perceive the environment and receive feedback, which provides the possibility to solve the problem of anti‐jamming decision‐making. This article regards the anti‐jamming measure as a kind of interaction behaviour and establishes the cognitive radar antagonistic environment model and uses the reinforcement learning algorithm to solve the problem of anti‐jamming decision‐making. Finally, this article verifies the feasibility of applying reinforcement learning theory on making anti‐jamming decision in the radar antagonistic environment model. The performance of different reinforcement learning algorithms is compared, and their advantages and disadvantages are discussed.

A Multiple-False-Target ISAR Shape Deception Jamming Method Based on Improved Template Multiplication Modulated Time-Delay Algorithm and Sub-Nyquist Sampling

In recent decades, the deception jamming approach based on digital radio frequency memory (DRFM) for inverse synthetic aperture radar (ISAR) has been a well-researched topic. Various types of jamming signals based on interrupted-sampling repeater jamming (ISRJ) can induce one-dimensional multiple high-resolution range profile (HRRP) false targets or two-dimensional realistic multiple ISAR false targets for deception. However, these existing methods generate false targets that are identical to the real target. The ISRJ false target also generates a main false target whose energy is much higher than other sub-false targets. Thus, it is easy to discover that the radar has suffered jamming. In order to generate better, more confusing jamming signals, this paper proposes a jamming method based on sub-Nyquist sampling jamming to induce realistic, multiple false targets on ISAR images. It improves a template multiplication modulated time-delay method to eliminate and add scatterers on the selected false target to change its ISAR shape. The frequency-shift parameters of the template jamming signal are analyzed and derived in detail for eliminating and adding scatterers at the specified location. Thus, multiple false targets with different ISAR shapes and similar energy are generated, which can create better deception effects. Meanwhile, this method can adjust the number of false targets and the location of changed-shape false targets. Furthermore, the resolution of the false target can adaptively change with the radar pulse width and the accumulated pulse number. The simulation results show that the proposed deception jamming strategy works.

The influence of three-aircraft formation blinking jamming on the performance of ground monopulse radar

The purpose of this study is to evaluate the jamming effectiveness evaluation against monopulse radar when using a non-coherent blinking jamming model at regular intervals based on three aircraft flight trajectories. To begin, the formation trajectory is solved under various aircraft attitudes to provide the radar cross section model for three-aircraft formation flying. Following that, the jamming angle tracking error model for three-aircraft formation flying is built and coupled with the features of monopulse radar angle tracking. The effective decoy angle and other criteria are used to measure the variance in jamming performance and the efficacy of formation flying radar jamming under varying jamming power and jamming gain. Finally, using the jamming effectiveness assessment criterion, the jamming effectiveness of formation flight is calculated. The findings demonstrate that the three-aircraft formation based on time transformation jamming model can successfully induce the monopulse radar angular tracking error, enhance it to 1.54 times, and stabilize the jamming effectiveness at 0.84, and it also has a relatively good jamming effect under straight flight trajectory and circular flight trajectory. Our jamming strategy will contribute to the design of electronic countermeasures, of which the findings of this research have practical significance for the application of new jamming styles.

A Covert Jamming Scheme Against an Intelligent Eavesdropper in Cooperative Cognitive Radio Networks

In this work, we design a covert jamming scheme against an intelligent Eve towards physical layer security in a cooperative cognitive radio networks. To protect the primary message from being decoded by an intelligent eavesdropper (Eve), a secondary user is picked as a friendly jammer that transmits artificial noise (AN) to the Eve. However, the intelligent Eve can detect the existence of AN, and thus the AN is inclined to be covertly transmitted at a lower detection probability, thereby mistaking the AN as background noise by the Eve. In this paper, firstly, by using a binary hypothesis testing, the detection error probability (DEP) is explicitly derived for detection performance analysis of the Eve in the perfect as well as statistical channel state information (CSI) scenario, separately. With the DEP, a covert jamming scheme is formulated to transmit the AN in both cases. Then, an optimization problem is formulated, and then recast into a two-part problem to maximize the secrecy performance with the covert jamming scheme in the perfect CSI case. Finally, the effective secrecy throughput is maximized under convert constraints in the statistical CSI case. Numerical results are provided to validate the DEP at the Eve and secrecy performance analysis.

Multi-Antenna Jammer-Assisted Secure Short Packet Communications in IoT Networks

In this work, we exploit a multi-antenna cooperative jammer to enable secure short packet communications in Internet of Things (IoT) networks. Specifically, we propose three jamming schemes to combat eavesdropping, i.e., the zero forcing beamforming (ZFB) scheme, null-space artificial noise (NAN) scheme, and transmit antenna selection (TAS) scheme. Assuming Rayleigh fading, we derive new closed-form approximations for the secrecy throughput with finite blocklength coding. To gain further insights, we also analyze the asymptotic performance of the secrecy throughput in the case of infinite blocklength. Furthermore, we investigate the optimization problem in terms of maximizing the secrecy throughput with the latency and reliability constraints to determine the optimal blocklength. Simulation results validate the accuracy of the approximations and evaluate the impact of key parameters such as the jamming power and the number of antennas at the jammer on the secrecy throughput.

Research on Efficient Multiagent Reinforcement Learning for Multiple UAVs’ Distributed Jamming Strategy

To support Unmanned Aerial Vehicle (UAV) joint electromagnetic countermeasure decisions in real time, coordinating multiple UAVs for efficiently jamming distributed hostile radar stations requires complex and highly flexible strategies. However, with the nature of the high complexity dimension and partial observation of the electromagnetic battleground, no such strategy can be generated by pre-coded software or decided by a human commander. In this paper, an initial effort is made to integrate multiagent reinforcement learning, which has been proven to be effective in game strategy generation, into the distributed airborne electromagnetic countermeasures domain. The key idea is to design a training simulator which close to a real electromagnetic countermeasure strategy game, so that we can easily collect huge valuable training data other than in the real battle ground which is sparse and far less than sufficient. In addition, this simulator is able to simulate all the necessary decision factors for multiple UAV coordination, so that multiagents can freely search for their optimal joint strategies with our improved Independent Proximal Policy Optimization (IPPO) learning algorithm which suits the game well. In the last part, a typical domain scenario is built to test, and the use case and experiment results manifest that the design is efficient in coordinating a group of UAVs equipped with lightweight jamming devices. Their coordination strategies are not only capable of handling given jamming tasks for the dynamic jamming of hostile radar stations but also beat expectations. The reinforcement learning algorithm can do some heuristic searches to help the group find the tactical vulnerabilities of the enemies and improve the multiple UAVs’ jamming performance.

Phased Array Radar Resource Consumption Method Based on Phase-Switched Screen

The consumption of phased array radar (PAR) resources affects the accuracy of its multi-target searching and tracking abilities. The jamming methods aimed at PAR involve deceptive jamming and comprehensive jamming strategies, but have the disadvantages of high cost and complexity. Given this context, this paper proposes a PAR resource consumption method based on phase-switched screen (PSS), and derives a relationship between the targets and the evaluation indicators. By implementing periodic modulation of PSS, a controlled number of deceptive multiple false targets can be generated, thereby enticing the radar system to engage in activities such as searching and tracking targets. When the quantity of false targets is increased, the number of beam requests becomes five times higher than compared to the scenario without false targets. This has also led the margin of time resources to exceed 50%, successfully achieving the objective of consuming radar resources. Furthermore, this method offers straightforward manipulation, flexibility, and the potential to significantly consume radar resources. The efficacy of the proposed method is confirmed with the simulation outcomes.

Strategy Optimization for Range Gate Pull-Off Track-Deception Jamming Under Black-Box Circumstance

In this paper, we study the strategy optimization problem of black-box range gate pull-off (RGPO) jamming. In the black-box RGPO jamming, the jammer does not have extensive knowledge about the tracking model of the threat radar, which makes it difficult to accurately estimate the performance of candidate jamming strategies. To address the issue, this paper proposes a multi-mode black-box RGPO jamming method, which uses a set of local elemental simulation models to cover the possible tracking model of the threat radar, and the overall performance estimate of the candidate jamming strategies are obtained by a certain combination of the sampling results from the local elemental simulation models. To obtain the desired RGPO jamming strategy, a particle swarm optimization with optimal computing budget allocation scheme (PSO-OCBA) based multi-mode black-box RGPO jamming strategy optimization algorithm is proposed. In addition, we construct several most widely used tracking problems as the benchmark problems to test the performance of the proposed method. Experimental results demonstrate that the proposed method is highly competitive to deal with the strategy optimization problem of the black-box RGPO jamming.

A Taxonomy of Radiofrequency Jamming and Spoofing Strategies and Criminal Motives

This article provides a taxonomy of radiofrequency (RF) jamming and spoofing tactics and strategies associated with specific criminal objectives. This is based on the fact that the motivations and strategies of cyber-attackers – predominantly financial – is well-known, but the motivations behind specific electromagnetic interferences is lacking in the current literature. Previous research has also overlooked the motivations of other actors using jamming and spoofing devices. The objective is to identify the most desirable spoofing and jamming strategies, likely targets, and likely motivations. The article finds that (a) previous literature on the subject overlooks a number of non-state actor motives, and therefore this research aims to fill this gap in knowledge; (b) out of 8 actors identified, denial of service attacks (7 out of 8), as well as so-called decoy spoofing (6), trojan spoofing (5), and jamming-enabled crime (5) are the most desirable strategies utilized; (c) out of 11 strategies identified, grey and black hat hackers (11 out of 11), terrorists (11) and activists (8), are likely to take advantage of most identified.

Cooperative encryption over backscatter: Secure green communication for two-way energy harvesting relay networks

This paper focuses on the design of a novel cooperative jamming strategy and resource allocation scheme in two-way energy harvesting (EH) relay networks. For solving the security vulnerability challenges, a backscatter jamming strategy is proposed by leveraging the information signals as the carrier, which can degrade the eavesdropping link without extra energy consumption, delay and strict synchronization requirement. Based on the above design, we formulate the secure energy efficiency (SEE) maximization problem by jointly optimizing the transmission powers of two sources, two different power splitting (PS) factors and time allocation factor. Although the resulting optimization problem is highly non-convex due to fractional objective function and multivariate coupling, an iterative algorithm is developed to convert the original problem into a tractable version and obtain the resource allocation solutions. Simulation results show that the achievable SEE of the proposed scheme is better than other conventional schemes.

A Stochastic Simulation Optimization-Based Range Gate Pull-Off Jamming Method

Range gate pull-off (RGPO) jamming is an electronic countermeasure widely used to fool radar tracking systems. Nevertheless, the research on its strategy optimization has only been tepid. Optimization model, appropriate performance metrics, as well as efficient algorithms are required to further the research in this field. The algebraic description for the objective function of the optimization of the RGPO jamming strategy is difficult to obtain, and the jamming results are not deterministic under the influence of the input noise. To address these issues, this paper models the generation of the RGPO jamming strategy as a stochastic simulation optimization (SSO) problem, and proposes an optimization algorithm of the RGPO jamming strategy with the help of SSO technologies. We propose the committee based active learning (CAL) assisted optimal computing budget allocation based particle swarm optimization (CALPSO-OCBA) to alleviate the competition between solution space search and candidate solution performance evaluation by embedding CAL into PSO-OCBA. To improve the feasibility of the proposed algorithm, a scoring scheme that does not rely on the internal knowledge of the radar tracking system (i.e., tracking model, tracking method and tracking parameters) is designed. In addition, we use four most widely used tracking problems as the benchmarks for testing the proposed optimization algorithm of the RGPO jamming strategy. Experimental results demonstrate that the proposed algorithm is highly competitive in solving the optimization problem of the RGPO jamming strategy.

Deep-Reinforcement-Learning-Based IRS for Cooperative Jamming Networks Under Edge Computing

Effective energy design and secure communications are critical in the Internet of Things (IoT). A cooperative jamming (CJ) scheme based on deep reinforcement learning (DRL) is proposed for intelligent reflecting surface (IRS) aided secure cooperative network with eavesdroppers. Our goal is to maximize the average secrecy rate, or energy efficiency with secrecy constraints by jointly optimising the transmit, jamming beamforming matrices and IRS phase-shift matrix. In order to figure out the challenging non-convex optimization problem, the deep deterministic policy gradient (DDPG) optimization algorithm is proposed to solve the energy efficiency maximization problem. In order to speed up task processing and reduce service delay, the edge computing server is employed for DRL training to improve computing speed and promote secure communication efficiency. By taking advantages of the features of proximity to end devices and computing resources, the edge devices provide a low latency efficient training support to the legal users and real-time control to the IRS units. By this way, a high security and low energy consumption IoT system is built. Numerical results show that the novel scheme improves the secrecy rate and energy efficiency compared with the benchmark scheme.

Cellular Network Enabled Energy-Harvesting Secure Communications for Full-Duplex D2D Links

This article investigates the secure transmissions for a wireless powered full-duplex (FD) device-to-device (D2D) overlay system. To coordinate the cellular downlink transmissions and the D2D transmissions, we propose a two-phase transmission protocol, where the first phase is dedicated to the transmissions of information and energy from the base station (BS) to its users and the devices, respectively, and the second phase is dedicated to information exchanging between the two devices over an FD link overheard by either active or passive eavesdroppers. In the case of active eavesdroppers, perfect channel state information (CSI) is assumed to be available, whereas for passive eavesdroppers, only statistic CSI is available. Both self-interference (SI) mitigation and BS-aided cooperative jamming are considered for the D2D secure transmissions. For system design, nonconvex power minimization problems under transmission rate constraints are formulated and convex approximated for efficiently obtaining numerical solutions. Simulation results show that the proposed scheme can well coordinate the cellular transmissions and the FD-D2D secure transmissions, while the cooperative jamming strategy can enhance the D2D transmission security, especially in the case of passive eavesdroppers.

Covert Communication for Integrated Satellite–Terrestrial Relay Networks with Cooperative Jamming

We explore the influence of cooperative jamming on covert communication for integrated satellite–terrestrial relay networks (ISTRN) in this article, where one full-duplex relay is utilized to support communications and acts as a jammer to confuse eavesdroppers. A cooperative scheme is first proposed to efficiently achieve covert communication in this specific scenario. Further, the probability of detection error (PDE) is derived. Subsequently, the minimum PDE is calculated using the optimal detection threshold. Moreover, a closed-form expression of outage probability (OP) is acquired to quantify the system’s covert performance. Based on this, we design a power optimization algorithm to enhance the effective covert rate under covertness constraints. Lastly, numerical simulation results are given to verify the efficiency of our cooperative jamming scheme when interrupting the eavesdroppers. Notably, an optimal power allocation factor can be obtained, leading to a higher effective covert rate.

Performance analysis of deep reinforcement learning-based intelligent cooperative jamming method confronting multi-functional networked radar

With the development of artificial intelligence technology, more and more intelligent countermeasure methods are applied in military confrontation fields to improve the intelligent level of weapons. Traditional radar jammers generate different jamming types by template matching, game theory or reasoning, which lack intelligent and adaptive jamming strategies in the battlefield environment with intelligent confrontation. To solve the intelligent decision-making problem of jammers in radar countermeasure, a cooperative jamming decision-making (CJDM) method based on reinforcement learning (RL) is proposed in this paper. The double deep Q network based on priority experience replay (PER-DDQN) is brought into the cooperative jamming strategy, and the CJDM model based on PER-DDQN is established in this paper. The scene of multiple jammers against multi-functional networked radar was built to simulate and analyze the performance of the proposed CJDM model based on PER-DDQN. The simulation results show that the proposed PER-DDQN can overcome the problem of data correlation and avoid unnecessary iteration, which is more suitable for sparse reward environment compared with deep Q network (DQN). Meanwhile, the proposed CJDM method based on PER-DDQN can effectively and intelligently realize optimal jamming decision-making.

Approximating Nash equilibrium for anti-UAV jamming Markov game using a novel event-triggered multi-agent reinforcement learning

In the downlink communication, it is currently challenging for ground users to cope with the uncertain interference from aerial intelligent jammers. The cooperation and competition between ground users and unmanned aerial vehicle (UAV) jammers leads to a Markov game problem of anti-UAV jamming. Therefore, a model-free method is adopted based on multi-agent reinforcement learning (MARL) to handle the Markov game. However, the benchmark MARL strategies suffer from dimension explosion and local optimal convergence. To solve these issues, a novel event-triggered multi-agent proximal policy optimization algorithm with Beta strategy (ETMAPPO) is proposed in this paper, which aims to reduce the dimension of information transmission and improve the efficiency of policy convergence. In this event-triggering mechanism, agents can learn to obtain appropriate observation in different moment, thereby reducing the transmission of valueless information. Beta operator is used to optimize the action search. It expands the search scope of policy space. Ablation simulations show that the proposed strategy achieves better global benefits with fewer dimension of information than benchmark algorithms. In addition, the convergence performance verifies that the well-trained ETMAPPO has the capability to achieve stable jamming strategies and stable anti-jamming strategies. This approximately constitutes the Nash equilibrium of the anti-jamming Markov game.

Anti-Jamming Games in Multi-Band Wireless Ad Hoc Networks

For multi-band wireless ad hoc networks of multiple users, an anti-jamming game between the users and a jammer is studied. In this game, the users (resp. jammer) want to maximize (resp. minimize) the expected rewards of the users taking into account various factors such as communication rate, hopping cost, and jamming loss. We analyze the arms race of the game and derive an optimal frequency hopping policy at each stage of the arms race based on the Markov decision process (MDP). It is analytically shown that the arms race reaches an equilibrium after a few rounds, and a frequency hopping policy and a jamming strategy at the equilibrium are characterized. We propose two kinds of collision avoidance protocols to ensure that at most one user communicates in each frequency band, and provide various numerical results that show the effects of the reward parameters and collision avoidance protocols on the optimal frequency hopping policy and the expected rewards at the equilibrium. Moreover, we discuss about equilibria for the case where the jammer adopts some unpredictable jamming strategies.

Parameterized and Large-Dynamic-Range 2-D Precise Controllable SAR Jamming: Characterization, Modeling, and Analysis

Barrage jamming technique with controllable jamming coverage against synthetic aperture radar (SAR) systems is of great importance in electronic countermeasures. However, it is still a difficulty for the jammer to accurately impose controllable two-dimensional (2-D) local jamming on the regions of interest (ROIs). In this respect, a new parameterized and large-dynamic-range precise controllable (PLDR-PC) jamming method has been proposed in this paper to assist in solving such problems. Based on the SAR imaging properties of linear frequency modulation (LFM) case, the range and azimuth modulation factors have been well designed to generate large dynamic controllable coverage of jamming signals with high 2-D processing gain. In such a context, the PLDR-PC technique can provide the optimal power allocation and considerably reduce the jamming power while still ensuring the satisfactory performance. The proposed PLDR-PC technique can improve the jamming efficiency and considerably reduce the exposure probability of the jammer. Moreover, to improve the barrage jamming performance, the parameter estimation error model is also established to determine the simple yet valid jamming strategy in practical implementations. Finally, extensive numerical simulations in comparison with the current jamming methods have been carried out to demonstrate the effectiveness and prospect of the PLDR-PC technique against airborne/spaceborne SAR systems.

Adaptive secure transmission for two‐way energy harvesting relay networks with two‐sided eavesdropping

AbstractIn this paper, for energy harvesting (EH) two‐way relay (TWR) networks with two‐sided eavesdroppers, the problem of secure information transmission is investigated. Based on this system model, a novel full‐duplex (FD) relaying jamming scheme is proposed to achieve simultaneously information reception, EH and jamming signal transmission at the cooperative relay. Particularly, the entire transmission phase and received signal at relay node are divided into two different time durations and three different parts by time allocation (TA) and power splitting (PS), respectively. For the aim of maximizing the sum secrecy rate, adaptive TA factor and PS factor are jointly optimized. We derive the non‐convex optimization function. To solve this highly non‐convex optimization problem, a heuristic approach based on particle swarm optimization (PSO) is proposed. Numerical results verify the superior sum secrecy rate of the proposed scheme. Besides, the impact of system parameters on the output performance is analyzed.

Adaptation of Frequency Hopping Interval for Radar Anti-Jamming Based on Reinforcement Learning

In modern electronic warfare, it is becoming very important to develop intelligent and adaptive radar anti-jamming methods since jammers can now launch increasingly complex and unpredictable attacks. Besides, in practice, the jamming strategy is usually unknown to the radar. To overcome the limitations caused by the lack of information about the jammer, reinforcement learning is applied to radar anti-jamming in this paper via the adaptation of frequency hopping interval. In reinforcement learning, the sequential decision problem to solve is described as a Markov Decision Process (MDP). To describe the sequential radar anti-jamming decision making process, a detailed radar anti-jamming MDP model is formulated. To balance between integration efficiency and probability of interception, a flexible adjustable tradeoff between them is devised by defining the reward function of the MDP as the weighted sum of the integration efficiency factor and the probability of interception factor. Two properties of the MDP value function are proved. These properties are used to derive the optimal frequency hopping time interval for different pulse widths under the RL framework. Simulation results show that the proposed radar anti-jamming strategy can adapt to the jamming environment well and can control its performance flexibly by adjusting the weights of integration efficiency and probability of interception.