基于DQN的探测干扰一体化波形优化设计

doi:10.12305/j.issn.1001-506X.2023.03.03

Abstract

Abstract:

In view that the reconnaissance jammer has the transmitting function, with the aim that the transmitted interference signal can also have the detection effect. The detection signal can be hidden in the jamming signal. A detection and jamming integrated signal waveform based on non-uniform intermittent sampling and repeated forwarding is proposed. Firstly, an integrated signal model is established, and then the non-uniform intermittent sampling and repeating forwarding technology is used to realize amplitude coding modulation. Secondly, in the optimization process, the characteristics of the integrated signal are analyzed from the fuzzy function and the radar detection link, and the corresponding objective function is constructed according to the distance, velocity resolution, and the ratio of the mean value to the standard deviation value of the post-pulse compression amplitude. Finally, the deep Q-learning algorithm is used to solve the objective function to obtain the optimal amplitude coding. The simulation results show that when the number of coding states is small, the convergence effect of deep Q-network(DQN) algorithm is consistent with that of the reinforcement learning algorithm. Compared with the genetic algorithm, the quality of optimal solution of DQN algorithm is improved by 13.10%. When the number of coding states increases, compared with genetic algorithm and reinforcement learning algorithm, the convergence value of DQN algorithm is larger and the optimal solution is more stable.

Key words: detection and jamming integrated signal, non-uniform intermittent sampling and repeated forwarding, fuzzy function, pulse amplitude coding, deep Q-learning

CLC Number:

TN97

Tao CHEN, Ying ZHANG, Xuejing HU, Yihan XIAO. Integrated waveform optimization design of detection and jamming based on DQN[J]. Systems Engineering and Electronics, 2023, 45(3): 638-646.

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References 20

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LFM调频信号参数	参数大小
信号长度T/μs	20
带宽B/MHz	10
采样频率f₀/MHz	40
调频斜率K	5×10¹¹
最小采样时间τ/μs	0.125

DQN网络参数	参数大小
学习率α	0.01
折现因子γ	0.9
记忆库总量	1 000
探索概率ε	0.9

指标	分组
指标	固定组	随机组1	随机组2	随机组3
收敛时间/s	111	99	324	225
收敛值	0.8251	0.8013	0.7928	0.8315

遗传算法参数	参数大小
种群个数	40
选择概率	0.5
杂交概率	0.7
变异率	0.001

DQN网络参数	参数大小
学习率α	0.01
折现因子γ	0.9
探索概率ε	0.9

Integrated waveform optimization design of detection and jamming based on DQN

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算法	最优解方差
遗传算法	0.016 4
强化学习算法	0.013 9
DQN算法	0.003 3

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