An Intelligent Decision-making Algorithm for Communication Countermeasure Jamming Resource Allocation

Hua XU; Bailin SONG; Lei JIANG; Ning RAO; Yunhao SHI

doi:10.11999/JEIT210115

Volume 43 Issue 11

Nov. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(11): 3086-3095

Hua XU, Bailin SONG, Lei JIANG, Ning RAO, Yunhao SHI. An Intelligent Decision-making Algorithm for Communication Countermeasure Jamming Resource Allocation[J]. Journal of Electronics & Information Technology, 2021, 43(11): 3086-3095. doi: 10.11999/JEIT210115

Citation:

Hua XU, Bailin SONG, Lei JIANG, Ning RAO, Yunhao SHI. An Intelligent Decision-making Algorithm for Communication Countermeasure Jamming Resource Allocation[J]. Journal of Electronics & Information Technology, 2021, 43(11): 3086-3095. doi: 10.11999/JEIT210115

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An Intelligent Decision-making Algorithm for Communication Countermeasure Jamming Resource Allocation

doi: 10.11999/JEIT210115

Information and Navigation College, Air Force Engineering University, Xi’an 710077, China

Received Date: 2021-02-01
Rev Recd Date: 2021-05-26

Available Online: 2021-06-11

Publish Date: 2021-11-23

Abstract

Abstract

Considering the intelligent decision of battlefield communication countermeasure, based on the overall confrontation, a Bootstrapped expert trajectory memory replay - Hierarchical reinforcement learning - Jamming resources distribution decision - Making algorithm(BHJM) is proposed, and the algorithm for frequency hopping jamming decision problem, according to the frequency distribution, jamming spectrum is divided, based on hierarchical reinforcement learning again decision jamming spectrum and bandwidth are divided, and finally based on the bootstrapped expert trajectory memory replay mechanism, the algorithm is optimized, the algorithm can is existing resources, especially under the condition of insufficient resources, give priority to jam the most threat target, obtain the optimal jamming effect and reduce the total jamming bandwidth. The simulation results show that, compared with the existing resource allocation decision algorithms, the proposed algorithm can save 25% of the resources of jammers and 15% of the jamming bandwidth, which is of great practical value.
- Intelligent interference decision,
- Hierarchical Reinforcement Learning(HRL),
- Jamming resource allocation,
- Expert trajectory

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References(17)

References

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