In the face of smart and varied jamming, intelligent radar anti-jamming technologies are urgently needed. Due to the variety of radar electronic counter-countermeasures (ECCMs), it is necessary to efficiently optimize ECCMs in the high-dimensional knowledge base to ensure that the radar achieves the optimal anti-jamming effect. Therefore, an intelligent radar anti-jamming decision-making method based on the deep deterministic policy gradient (DDPG) and the multi-agent deep deterministic policy gradient (MADDPG) (DDPG-MADDPG) algorithm is proposed. Firstly, by establishing a typical working scenario of radar and jamming, we designed the intelligent radar anti-jamming decision-making model, and the anti-jamming decision-making process was formulated. Then, aiming at different jamming modes, we designed the anti-jamming improvement factor and the correlation matrix of jamming and ECCM. They were used to evaluate the jamming suppression performance of ECCMs and to provide feedback for the decision-making algorithm. The decision-making constraints and four different decision-making objectives were designed to verify the performance of the decision-making algorithm. Finally, we designed a DDPG-MADDPG algorithm to generate the anti-jamming strategy. The simulation results showed that the proposed method has excellent robustness and generalization performance. At the same time, it has a shorter convergence time and higher anti-jamming decision making accuracy. 

Radar Anti-Jamming Decision-Making Method Based on DDPG-MADDPG Algorithm

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 ε-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.

https://www.mdpi.com/2072-4292/15/12/3108/pdf?version=1686797937

A Cognitive Electronic Jamming Decision-Making Method Based on Q-Learning and Ant Colony Fusion Algorithm

Unmanned aerial vehicle (UAV) swarm networking and collaboration have significant prospects in both civilian and military applications, due to its remarkable properties in cooperative efficiency, reduced risks, and operational cost. Traditional algorithms have challenging issues of high computational complexity and low efficiency in UAV swarm networking and collaboration, while computational intelligence (CI) has attracted increasing attention since it has advantages in solving complex optimization problems. The networking of UAV swarms serves as an essential foundation for collaboration, and intelligent collaboration is a crucial means of enhancing the performance of UAV swarm systems. To date, extensive CI-based algorithms have been proposed to improve the networking and collaboration capabilities of UAV swarms, and several relevant surveys have also been presented. However, existing surveys either review networking or collaboration. To the best of our knowledge, there is no survey that simultaneously concentrates on CI-based UAV swarm networking and collaboration. In this survey, we provide a comprehensive overview of CI-based networking and collaboration algorithms from six typical aspects including channel access, network routing, cooperative task assignment, cooperative path planning, cooperative search, and cooperative jamming. More importantly, to help researchers choose appropriate algorithms to satisfy the requirements of different missions, we classify CI-based algorithms into four categories, namely heuristic behavior search-based algorithms, policy design-based algorithms, policy learning-based algorithms, and hybrid algorithms. Finally, we discuss open issues and future directions that may influence future research on UAV swarm intelligence networking and collaboration. This review may provide new insights and valuable references for researchers in this field.

Computational Intelligence Algorithms for UAV Swarm Networking and Collaboration: A Comprehensive Survey and Future Directions

With the development of jamming systems, the jammer is becoming much smarter and can change its strategy with radar, which forms a competitive game between the radar and the jammer and poses a significant threat to the radar. In this letter, the antijamming game of frequency-agile (FA) radar is investigated, in which the jammer is capable of transmitting jamming signals with multiple frequency channels and different power allocation manners. To characterize the sequential interaction and partial observation of the antijamming game, an extensive-form game (EFG) is introduced to model the confrontation between the radar and the jammer. Subsequently, a novel multiagent reinforcement learning (MARL) algorithm, termed neural fictitious self-play (NFSP)-deep deterministic policy gradient (DDPG), is devised by combining NFSP with DDPG and modifying the supervised learning process of NFSP to solve Nash equilibrium (NE) strategies, which can handle the antijamming games featuring both discrete and continuous action spaces. Finally, simulation results show that the learning strategies acquired through the proposed method approximate NE and outperform the rule-based strategies. The signal-to-interference-pulse-noise ratio (SINR) improvements of radar taking NE strategy are 9.13, 9.12, and 9.15 dB in the worst case in the antijamming game with five frequencies compared to three rule-based strategies.

Multiagent Reinforcement Learning for Antijamming Game of Frequency-Agile Radar

Advancing UAV security with artificial intelligence: A comprehensive survey of techniques and future directions

In modern electronic warfare, the intelligence of the jammer greatly worsens the anti-jamming performance of traditional passive suppression methods. How to actively design anti-jamming strategies to deal with intelligent jammers is crucial to the radar system. In the existing research on radar anti-jamming strategies’ design, the assumption of jammers is too ideal. To establish a model that is closer to real electronic warfare, this paper explores the intelligent game between a subpulse-level frequency-agile (FA) radar and a transmit/receive time-sharing jammer under jamming power dynamic allocation. Firstly, the discrete allocation model of jamming power is established, and the multiple-round sequential interaction between the radar and the jammer is described based on an extensive-form game. A detection probability calculation method based on the signal-to-interference-pulse-noise ratio (SINR) accumulation gain criterion (SAGC) is proposed to evaluate the game results. Secondly, considering that the competition between the radar and the jammer has the feature of imperfect information, we utilized neural fictitious self-play (NFSP), an end-to-end deep reinforcement learning (DRL) algorithm, to find the Nash equilibrium (NE) of the game. Finally, the simulation results showed that the game between the radar and the jammer can converge to an approximate NE under the established model. The approximate NE strategies are better than the elementary strategies from the perspective of detection probability. In addition, comparing NFSP and the deep Q-network (DQN) illustrates the effectiveness of NFSP in solving the NE of imperfect information games.

/pdf/radar-and-jammer-intelligent-game-under-jamming-power-3dkpwb0d.pdf

Radar and Jammer Intelligent Game under Jamming Power Dynamic Allocation

In modern electronic warfare, the jamming strategy is more complex than before and the jammer is capable of changing its strategy. Traditional anti-jamming strategy learning methods apply reinforcement learning (RL) and cannot handle the non-stationary jamming strategy. To address that issue, we propose a method that combines RL and supervised learning (SL) to design an anti-jamming strategy for frequency-agile (FA) radar. Firstly, we consider three jamming strategies, which include changes in the duration and type of jamming. And the non-stationary jammer is described by the uncertainty of the jamming strategy. Secondly, by analyzing the non-stationary characteristics of the jamming strategy, an anti-jamming algorithm of joint RL and SL is proposed, and we give the specific process of the algorithm. Finally, the simulation experiments demonstrate that the proposed method can effectively deal with the dynamically changing mainlobe interference, and the anti-jamming performance of the proposed method is robust.

Reinforcement Learning Based Radar Anti-Jamming Strategy Design against a Non-Stationary Jammer

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Radar and Jammer Intelligent Game under Jamming Power Dynamic Allocation

Multiagent Reinforcement Learning for Antijamming Game of Frequency-Agile Radar

Reinforcement Learning Based Radar Anti-Jamming Strategy Design against a Non-Stationary Jammer