Search Results (17)

In complex scenarios, radar echoes are contaminated by strong jamming, which significantly degrades their detection. Target detection under ultra-low signal-to-jamming ratio (SJR) conditions has thus become a major challenge when confronted with active jamming represented by smeared spectrum (SMSP) noise. Traditional jamming suppression methods are often limited by model dependency and useful signal loss. Convolutional neural networks (CNNs) have gained significant attention as an effective method for jamming suppression. However, in an ultra-low SJR environment, CNNs would have difficulty in carrying out jamming suppression, resulting in poor signal quality. In this study, we utilize a bandwidth enhancement method to allow CNNs to perform effective radar active jamming suppression in ultra-low SJR environments. Specifically, the bandwidth enhancement method reduces the correlation between target and jamming signals, which yields higher-quality target range profiles. Consequently, a modified CNN featuring a dense connection module can effectively suppress jamming even in ultra-low SJR scenarios. The experimental results show that when the input SJR is −30 dB and the bandwidth is 1.2 GHz, the output SJR reaches 13.25 dB. Meanwhile, the improvement factor (IF) gradually increases and reaches saturation at ~15 dB. Building on the bandwidth enhancement method, the modified CNN further improves the IF by ~27 dB. This work is expected to offer a new technical pathway for suppressing radar active jamming in ultra-low SJR scenarios. Full article

Forward jammers replicate and retransmit radar signals back to generate coherent jamming signals and false targets, making anti-jamming an urgent issue in electronic warfare. Jamming transmitters work at saturation to maximize the retransmission power such that only the phase information of the angular waveform at the designated direction of arrival (DOA) is retained. Therefore, amplitude modulation of MIMO radar angular waveforms offers an advantage in combating forward jamming. We address both the design of unimodular MIMO waveforms and their associated mismatch filters to confront mainlobe jamming in this paper. Firstly, we design the MIMO waveforms to maximize the discrepancy between the retransmitted jamming and the spatially synthesized radar signal. We formulate the problem as unconstrained non-linear optimization and solve it using the conjugate gradient method. Particularly, we introduce fast Fourier transform (FFT) to accelerate the numeric calculation of both the objection function and its gradient. Secondly, we design a mismatch filter to further suppress the filtered jamming through convex optimization in polynomial time. The simulation results show that for an eight-element MIMO radar, we are able to reduce the correlation between the angular waveform and saturated forward jamming to −6.8 dB. Exploiting this difference, the mismatch filter can suppress the jamming peak by 19 dB at the cost of an SNR loss of less than 2 dB. Full article

Unmanned aerial vehicle (UAV) swarm confrontation jamming offers a cost-effective and long-range countermeasure against hostile swarms. Intelligent decision-making is a key factor in ensuring its effectiveness. In response to the low-timeliness problem caused by linear programming in current algorithms, this paper proposes an intelligent decision-making algorithm for UAV swarm confrontation jamming based on the multi-agent actor–critic (M2AC) model. First, based on Markov games, an intelligent mathematical decision-making model is constructed to transform the confrontation jamming scenario into a symbolized mathematical problem. Second, the indicator function under this learning paradigm is designed by combining the actor–critic algorithm with Markov games. Finally, by employing a reinforcement learning algorithm with multithreaded parallel training–contrastive execution for solving the model, a Markov perfect equilibrium solution is obtained. The experimental results indicate that the algorithm based on M2AC can achieve faster training and decision-making speeds, while effectively obtaining a Markov perfect equilibrium solution. The training time is reduced to less than 50% compared to the baseline algorithm, with decision times maintained below 0.05 s across all simulation conditions. This helps alleviate the low-timeliness problem of UAV swarm confrontation jamming intelligent decision-making algorithms under highly dynamic real-time conditions, leading to more effective and efficient UAV swarm operations in various jamming and electronic warfare scenarios. Full article

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. Full article

When wireless communication networks encounter jamming attacks, they experience spectrum resource occupation and data communication failures. In order to address this issue, an anti-jamming algorithm based on distributed multi-agent reinforcement learning is proposed. Each terminal observes the spectrum state of the environment and takes it as an input. The algorithm then employs Q-learning, along with the primary and backup channel allocation rules, to finalize the selection of the communication channel. The proposed algorithm designs primary and backup channel allocation rules for sweep jamming and smart jamming strategies. It can predict the behavior of jammers while reducing decision conflicts among terminals. The simulation results demonstrate that, in comparison to existing methods, the proposed algorithm not only enhances data transmission success rates across multiple scenarios but also exhibits superior operational efficiency when confronted with jamming attacks. Overall, the anti-jamming performance of the proposed algorithm outperforms the comparison methods. Full article

Wireless sensor networks (WSNs), integral components underpinning the infrastructure of the internet of things (IoT), confront escalating threats originating from attempts at malicious jamming. Nevertheless, the limited nature of the hardware resources in distributed, low-cost WSNs, such as those for computing power and storage, poses a challenge when implementing complex and intelligent anti-jamming algorithms like deep reinforcement learning (DRL). Hence, in this paper a rapid anti-jamming method is proposed based on imitation learning in order to address this issue. First, on-network nodes obtain expert anti-jamming trajectories using heuristic algorithms, taking historical experiences into account. Second, an RNN neural network that can be used for anti-jamming decision making is trained by mimicking these expert trajectories. Finally, the late-access network nodes receive anti-jamming network parameters from the existing nodes, allowing them to obtain a policy network directly applicable to anti-jamming decision making and thus avoiding redundant learning. Experimental results demonstrate that, compared with traditional Q-learning and random frequency-hopping (RFH) algorithms, the imitation learning-based algorithm empowers late-access network nodes to swiftly acquire anti-jamming strategies that perform on par with expert strategies. Full article

Traditional intelligent frequency-hopping anti-jamming technologies typically assume the presence of an ideal control channel. However, achieving this ideal condition in real-world confrontational environments, where the control channel can also be jammed, proves to be challenging. Regrettably, in the absence of a reliable control channel, the autonomous synchronization of frequency decisions becomes a formidable task, primarily due to the dynamic and heterogeneous nature of the transmitter and receiver’s spectral states. To address this issue, a novel communication framework for intelligent frequency decision is introduced, which operates without the need for negotiations. Furthermore, the frequency decision challenge between two communication terminals is formulated as a stochastic game, with each terminal’s utility designed to meet the requirements of a potential game. Subsequently, a two-agent deep reinforcement learning algorithm for best-response policy learning is devised to enable both terminals to achieve synchronization while avoiding jamming signals. Simulation results demonstrate that once the proposed algorithm converges, both communication terminals can effectively evade jamming signals. In comparison to existing similar algorithms, the throughput performance of this approach remains largely unaffected, with only a slightly extended convergence time. Notably, this performance is achieved without the need for negotiations, making the presented algorithm better suited for realistic scenarios. Full article

This paper proposes an optimization method to improve the radar anti-jamming strategy by using the predictability of left game interval. Firstly, we propose the concept of key time nodes in radar/jammer confrontation and analyze its predictability. Secondly, we analyze the radar-winning scenarios by considering the temporal constraints and construct the actual utility matrix of the radar. Then, we describe the optimization algorithm using radar-winning probability statistics based on the prediction of left game interval. Finally, we carry out a simulation experiment by comparing it with other anti-jamming strategies to verify the rationality, and the result shows that the proposed method can significantly improve the radar’s winning probability in the confrontation. By using the proposed anti-jamming strategy optimization method just at the key time nodes, the imperceptibility from the jammer is improved, and its long-term superiority can be maintained in the confrontation. Full article

In order to improve the efficiency and adaptability of cognitive radar jamming decision-making, a fusion algorithm (Ant-QL) based on ant colony and Q-Learning is proposed in this paper. The algorithm does not rely on a priori information and enhances adaptability through real-time interactions between the jammer and the target radar. At the same time, it can be applied to single jammer and multiple jammer countermeasure scenarios with high jamming effects. First, traditional Q-Learning and DQN algorithms are discussed, and a radar jamming decision-making model is built for the simulation verification of each algorithm. Then, an improved Q-Learning algorithm is proposed to address the shortcomings of both algorithms. By introducing the pheromone mechanism of ant colony algorithms in Q-Learning and using the $\epsilon$-greedy algorithm to balance the contradictory relationship between exploration and exploitation, the algorithm greatly avoids falling into a local optimum, thus accelerating the convergence speed of the algorithm with good stability and robustness in the convergence process. In order to better adapt to the cluster countermeasure environment in future battlefields, the algorithm and model are extended to cluster cooperative jamming decision-making. We map each jammer in the cluster to an intelligent ant searching for the optimal path, and multiple jammers interact with each other to obtain information. During the process of confrontation, the method greatly improves the convergence speed and stability and reduces the need for hardware and power resources of the jammer. Assuming that the number of jammers is three, the experimental simulation results of the convergence speed of the Ant-QL algorithm improve by 85.4%, 80.56% and 72% compared with the Q-Learning, DQN and improved Q-Learning algorithms, respectively. During the convergence process, the Ant-QL algorithm is very stable and efficient, and the algorithm complexity is low. After the algorithms converge, the average response times of the four algorithms are 6.99 × 10⁻⁴ s, 2.234 × 10⁻³ s, 2.21 × 10⁻⁴ s and 1.7 × 10⁻⁴ s, respectively. The results show that the improved Q-Learning algorithm and Ant-QL algorithm also have more advantages in terms of average response time after convergence. Full article

Attacking a naval vessel with multiple missiles is an important way to improve the hit rate of missiles. Missile-borne radars need to complete detection and antijamming tasks to guide missiles, but communication between these radars is often difficult. In this paper, an optimization method based on multi-agent reinforcement learning is proposed for the collaborative detection and antijamming tasks of multiple radars against one naval vessel. We consider the collaborative radars as one player to make their confrontation with the naval vessel a two-person zero-sum game. With temporal constraints of the radar’s and jammer’s recognition and preparation interval, the game focuses on taking a favorable position at the end of the confrontation. It is assumed the total jamming capability of a shipborne jammer is constant and limited, and the shipborne jammer allocates the jamming capability in the radar’s direction according to the radar threat assessment result and its probability of successful detection. The radars work collaboratively through prior centralized training and obtain a good performance by decentralized execution. The proposed method can make radars collaborate to detect the naval vessel, rather than only considering the detection result of each radar itself. Experimental results show that the proposed method in this paper is effective, improving the winning probability to 10% and 25% in the two-radar and four-radar scenarios, respectively. Full article

In an electronic jamming system, the ability to adequately perceive information determines the effectiveness of an electronic countermeasures strategy. This paper proposes a new method based on the combination of a multi-agent electronic jammer and an information sharing mechanism. With the development of intelligent technology and deep learning, these technologies have been applied in electronic countermeasure game systems. Introducing intelligent technology into the electronic confrontation system can greatly improve decision-making efficiency. At the same time, a multi-agent electronic countermeasure cooperative system based on the information sharing method can break through the limited information perception capabilities of a single agent, thereby greatly improving the survivability of jamming systems in electronic warfare. Experimental results show that our method requires a lower jamming-to-signal ratio than the single jammer method to achieve effective electronic jamming. In addition, the electronic jamming parameters can be updated automatically as the external electromagnetic environment changes quickly, realizing a more intelligent electronic jamming system. Full article

Current electronic warfare jammers and radar countermeasures are characterized by dynamism and uncertainty. This paper focuses on a decision-making framework of radar anti-jamming countermeasures. The characteristics and implementation process of radar intelligent anti-jamming systems are analyzed, and a scheduling method for radar anti-jamming action based on the Partially Observable Markov Process (POMDP) is proposed. The sample-based belief distribution is used to reflect the radar’s cognition of the environment and describes the uncertainty of the recognition of jamming patterns in the belief state space. The belief state of jamming patterns is updated with Bayesian rules. The reward function is used as the evaluation criterion to select the best anti-jamming strategy, so that the radar is in a low threat state as often as possible. Numerical simulation combines the behavioral prior knowledge base of radars and jammers and obtains the behavioral confrontation benefit matrix from the past experience of experts. The radar controls the output according to the POMDP policy, and dynamically performs the best anti-jamming action according to the change of jamming state. The results show that the POMDP anti-jamming policy is better than the conventional policy. The POMDP approach improves the adaptive anti-jamming capability of the radar and can quickly realize the anti-jamming decision to jammers. This work provides some design ideas for the subsequent development of an intelligent radar. Full article

Satellite communication systems are increasingly facing serious environmental challenges such as malicious jamming, monitoring, and intercepting. As a current development of artificial intelligence, intelligent jammers with learning ability can effectively perceive the surrounding spectrum environment to dynamically change their jamming strategies. As a result, the current mainstream satellite communication anti-jamming technology based on wide interval high-speed frequency hopping is unable to deal with this problem effectively. In this work, we focus on anti-jamming problems in the satellite communication domain, and reformulate the power allocation problem under two kinds of confrontation scenarios as one-shot and repeated games model. Specifically, for the problem of multi-channel power allocation under a one-shot confrontation scenario, we firstly model the problem of allocating limited power resource between communication parties and a jammer on multi-channel based on a BG (Blotto Game) model. Secondly, a DO-SINR (Double Oracle-Signal to Interference plus Noise Ratio) algorithm is designed to approximate the Nash equilibrium of the game between two parties. Experiments show that the DO-SINR algorithm can effectively obtain the approximate Nash equilibrium of the game. For the problem of multi-channel power allocation under a repeated confrontation scenario, we firstly transform the problem into an online shortest path problem with a graph structure to make the problem solving process more intuitive, and then design the Exp3-U (Exp3-Uniform) algorithm which utilizes the graph structure to solve the multi-channel power allocation problem. Experiments show that our algorithm can minimize the expected regret of communication parties during online confrontation, while maintaining good operating efficiency. The two power allocation problems constructed in this paper are common problem formed in confrontation scenarios. Our research and analysis can simulate some actual confrontation scenarios of the satellite communication power allocation, which can be used to improve the adaptability of satellite communication systems in complex environments. Full article

Shear jamming, a relatively new type of phase transition from discontinuous shear thickening into a solid-like state driven by shear in dense suspensions, has been shown to originate from frictional interactions between particles. However, not all dense suspensions shear jam. Dense fumed silica colloidal systems have wide applications in the industry of smart materials from body armor to dynamic dampers due to extremely low bulk density and high colloid stability. In this paper, we provide new evidence of shear jamming in polypropylene glycol/fumed silica suspensions using optical in situ speed recording during low-velocity impact and explain how it contributes to impact absorption. Flow rheology confirmed the presence of discontinuous shear thickening at all studied concentrations. Calculations of the flow during impact reveal that front propagation speed is 3–5 times higher than the speed of the impactor rod, which rules out jamming by densification, showing that the cause of the drastic impact absorption is the shear jamming. The main impact absorption begins when the jamming front reaches the boundary, creating a solid-like plug under the rod that confronts its movement. These results provide important insights into the impact absorption mechanism in fumed silica suspensions with a focus on shear jamming. Full article

Fifth-generation (5G) communication technology is intended to offer higher data rates, outstanding user exposure, lower power consumption, and extremely short latency. Such cellular networks will implement a diverse multi-layer model comprising device-to-device networks, macro-cells, and different categories of small cells to assist customers with desired quality-of-service (QoS). This multi-layer model affects several studies that confront utilizing interference management and resource allocation in 5G networks. With the growing need for cellular service and the limited resources to provide it, capably handling network traffic and operation has become a problem of resource distribution. One of the utmost serious problems is to alleviate the jamming in the network in support of having a better QoS. However, although a limited number of review papers have been written on resource distribution, no review papers have been written specifically on 5G resource allocation. Hence, this article analyzes the issue of resource allocation by classifying the various resource allocation schemes in 5G that have been reported in the literature and assessing their ability to enhance service quality. This survey bases its discussion on the metrics that are used to evaluate network performance. After consideration of the current evidence on resource allocation methods in 5G, the review hopes to empower scholars by suggesting future research areas on which to focus. Full article