Radar Anti-Jamming Countermeasures Intelligent Decision-Making: A Partially Observable Markov Decision Process Approach

Xing, Huaixi; Qing-hua, Xing; Wang, Kun

doi:10.3390/aerospace10030236

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…To verify the training effect of the radar anti-jamming decision-making method based on the DDPG-MADDPG algorithm, it is compared with the anti-jamming decision-making method based on the DQN-MADQN algorithm [47] and random decision-making algorithm [40]. The structure of the DQN-MADQN algorithm is the same as that of the DDPG-MADDPG algorithm.…”

Section: Resultsmentioning

confidence: 99%

“…Aiming at the diversity of jamming and radar ECCMs, a unified quantitative evaluation method was proposed by combining robust time-frequency analysis (RTFA) and peak-to-average power ratio (PAPR) [39]. In the face of the dynamic and uncertain characteristics of jamming and radar countermeasures, the jamming threat level was defined and used as an evaluation criterion to select the best anti-jamming strategy [40]. The abovementioned research studied different evaluation factors in different problems, and one ECCM can suppress multiple forms of jamming to produce different anti-jamming effects.…”

Section: Introductionmentioning

confidence: 99%

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

Wei

Yan

et al. 2023

Remote Sensing

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Wei

Yan

et al. 2023

Remote Sensing

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“…This manuscript uses a Markov Decision Process (MDP) [22][23][24] to solve the model. For quadruples S, A, P, r , the state space S can be expressed as follows:…”

Section: Problem Formulationmentioning

confidence: 99%

Anti-Jamming Communication Using Slotted Cross Q Learning

Niu

Zhou

et al. 2023

Electronics

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Most of the existing intelligent anti-jamming communication algorithms model sensing, learning, and transmission as a serial process, and ideally assume that the duration of sensing and learning timeslots is very short, almost negligible. However, when the jamming environment changes rapidly, the sensing and learning time can no longer be ignored, and the adaptability of the wireless communication system to the time-varying jamming environment will be significantly reduced. To solve this problem, this paper proposes a parallel Q-learning (PQL) algorithm. In the case of long sensing and learning time, by modeling sensing, learning, and transmission as parallel processes, the time that the transmitter remains silent during sensing and learning is reduced. Aiming at the situation that the PQL algorithm is susceptible to jamming when the jamming changes faster, this paper proposes an intelligent anti-jamming algorithm for wireless communication based on Slot Cross Q-learning (SCQL). In the case of rapid change of jamming channel, the system can sense and learn the jamming patterns in multiple successive jamming periods at the same time in the same timeslot, and use multiple Q-tables to learn the jamming patterns in different jamming periods, so as to achieve the effect of reliable communication in the environment with rapid change of jamming. The simulation results show that the jamming collision rate of the proposed algorithm under the condition of intelligent blocking jamming is equivalent to that of the traditional Q-learning (QL), but the timeslot utilization rate is higher. Compared with PQL, the proposed algorithm has the same slot utilization and lower jamming collision rate. Compared with random frequency hopping (RFH) anti-jamming, the proposed algorithm not only has higher timeslot utilization, but also has lower jamming collision rate.

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Radar Anti-Jamming Countermeasures Intelligent Decision-Making: A Partially Observable Markov Decision Process Approach

Cited by 2 publications

References 12 publications

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

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

Anti-Jamming Communication Using Slotted Cross Q Learning

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