Collaborative Jamming Decision-Making Mechanism Using Ant Colony Algorithm in Electromagnetic Antagonism

Gu, Wanyi; Zhu, Lianqing; Bu, Yuehang; Yue, Wenwei; Cai, Xuelian; Fan, Yonggao

doi:10.1109/icct50939.2020.9295683

Cited by 2 publications

(3 citation statements)

References 8 publications

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“…Ye et al [21,22] proposed a cognitive collaborative jamming decision-making method based on a swarm algorithm, which finds the global optimal solution through the process of searching for quality resources using a swarm. There are many similar population intelligence algorithms, such as genetic algorithms [23,24], ant colony algorithms [25], differential evolutionary algorithms [26][27][28] and water wave optimization algorithms [29]. All these algorithms can be useful for solving jamming decision-making models, but the autonomy, real time use and accuracy cannot fully meet the requirements of CEW.…”

Section: Traditional Radar Jamming Decision-making Methodsmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Traditional Radar Jamming Decision-making Methodsmentioning

confidence: 99%

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

et al. 2023

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“…Step 6. Analyze the changes in the radar threat level before and after the jamming and calculate the reward value of the jamming action by equation (10).…”

Section: Cognitive Electronic Jamming Decision-makingmentioning

confidence: 99%

“…Ye et al [6,7] proposed a cognitive collaborative jamming decisionmaking method based on bee colony algorithm, which finds the globally optimal solution through the process of bee colony searching for high-quality resources. Similarly, there are swarm intelligence algorithms such as genetic algorithm [8,9], ant colony algorithm [10], and heuristic algorithms such as differential evolution algorithm [11][12][13] and water wave optimization algorithm [14]. However, these algorithms cannot consider all possible jamming factors when solving the jamming decision model, and the decision accuracy needs to be improved.…”

Section: Introductionmentioning

confidence: 99%

Cognitive Electronic Jamming Decision-Making Method Based on Improved $Q$ -Learning Algorithm

et al. 2021

International Journal of Aerospace Engineering

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In this paper, a cognitive electronic jamming decision-making method based on improved Q -learning is proposed to improve the efficiency of radar jamming decision-making. First, the method adopts the simulated annealing (SA) algorithm’s Metropolis criterion to enhance the exploration strategy, balancing the contradictory relationship between exploration and utilization in the algorithm to avoid falling into local optima. At the same time, the idea of stochastic gradient descent with warm restarts (SGDR) is introduced to improve the learning rate of the algorithm, which reduces the oscillation and improves convergence speed at the later stage of the algorithm iteration. Then, a cognitive electronic jamming decision-making model is constructed, and the improved Q -learning algorithm’s specific steps are given. The simulation experiment takes a multifunctional radar as an example to analyze the influence of exploration strategy and learning rate on decision-making performance. The results reveal that compared with the traditional Q -learning algorithm, the improved Q -learning algorithm proposed in this paper can fully explore and efficiently utilize and converge the results to a better solution at a faster speed. The number of iterations can be reduced to more than 50%, which proves the feasibility and effectiveness of the method applied to cognitive electronic jamming decision-making.

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Collaborative Jamming Decision-Making Mechanism Using Ant Colony Algorithm in Electromagnetic Antagonism

Cited by 2 publications

References 8 publications

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

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

Cognitive Electronic Jamming Decision-Making Method Based on Improved $Q$ -Learning Algorithm

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Collaborative Jamming Decision-Making Mechanism Using Ant Colony Algorithm in Electromagnetic Antagonism

Cited by 2 publications

References 8 publications

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

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

Cognitive Electronic Jamming Decision-Making Method Based on Improved Q -Learning Algorithm

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Cognitive Electronic Jamming Decision-Making Method Based on Improved $Q$ -Learning Algorithm