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A Radar Anti-jamming Method under Multi-jamming Scenarios Based on Deep Reinforcement Learning in Complex Domains
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摘要: 在现代电子战中，雷达面临的干扰环境比以前更加复杂，机载干扰机会根据突袭任务与突袭阶段的不同而改变其干扰方式。近年来，基于强化学习的雷达抗干扰方法在单一干扰对抗场景下取得了一定进展，但在实际复杂多干扰场景下的研究仍有不足。为了解决该问题，本文提出了一种基于复数域深度强化学习的多干扰场景雷达抗干扰方法以优化频率捷变雷达的抗干扰策略。首先，针对突袭任务的阶段性特点建立了噪声瞄准干扰、距离假目标欺骗干扰与密集假目标转发干扰3种干扰模型，并设计了3种干扰顺序策略来模拟实际干扰场景。其次，针对多干扰场景模型，构建了一种融合信干噪比与目标航迹完整性的强化学习奖励函数，并针对干扰信号的复数域特征，提出了一种基于复数域深度强化学习的多干扰场景雷达抗干扰方法。最后，基于3种干扰顺序策略设计了雷达抗干扰仿真实验，结果表明，所提方法能够有效解决雷达面临的时序条件下复杂多干扰场景的主瓣干扰问题，与两种经典深度强化学习算法相比该方法抗干扰决策性能大幅提高，平均决策时间降低至405.3 ms。Abstract: In modern electronic warfare, the jamming environment of radar is more complex than ever. The airborne jammer adapts its jamming method based on diverse raid missions and stages. Recently, the reinforcement learning–based radar anti-jamming method has made some progress in the confrontation scenario of single jamming; however, the gap with respect to actual complex multi-jamming scenarios is large. To address this issue, this paper proposes a multi-jamming scenario radar anti-jamming method based on deep reinforcement learning in the complex domain to optimize the anti-jamming strategy of frequency agile radar. First, according to the stage characteristics of the raid mission, noise spot jamming, range deception jamming , and dense false target forwarding jamming models are established. The three jamming sequence strategies were designed to simulate actual jamming scenarios. Second, a reinforcement learning reward function that integrates the signal-to-noise ratio and target trajectory integrity is constructed for the multi-jamming scenario model. Thus, a multi-jamming scenario radar anti-jamming method based on deep reinforcement learning in a complex domain is proposed, which is based on the complex domain characteristics of the jamming signal. Finally, radar anti-jamming simulation experiments are performed based on the three jamming sequence strategies. The results show that the proposed method can effectively deal with the main-lobe jamming problem of complex multi-jamming scenarios under time-sequence conditions. Moreover, the average decision-making accuracy was improved, and the average decision-making time was reduced to 405.3 ms compared with the two classical reinforcement learning algorithms.
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图 3 距离假目标欺骗干扰仿真图
Figure 3. Simulation diagram of distance false-target deception jamming
图 10 基于复数域深度强化学习的多干扰场景雷达抗干扰网络
Figure 10. Deep RL based radar anti-jamming network under multi-jamming scenes in complex domain
图 15 3种干扰类型下不同强化学习算法的决策性能
Figure 15. Decision performance of different RL algorithms under three types of interference
图 16 DRL-ANCD网络对于3种干扰类型的抗干扰行为决策
Figure 16. Anti-jamming decisions of DRL-ANCD networks for three interference
图 17 3种干扰策略下不同强化学习算法的决策性能
Figure 17. Decision performance of different RL algorithms under three interference strategies
图 18 干扰策略Ⅰ下DRL-ANCD网络的抗干扰行为
Figure 18. Anti-jamming behaviors of DRL-ANCD networks under interference strategy I
图 19 干扰策略Ⅱ下DRL-ANCD网络的抗干扰行为
Figure 19. Anti-jamming behaviors of DRL-ANCD networks under interference strategy Ⅱ
图 20 干扰策略Ⅲ下DRL-ANCD网络的抗干扰行为
Figure 20. Anti-jamming behaviors of DRL-ANCD networks under interference strategy Ⅲ
1 深度确定性策略梯度算法
1. Deep deterministic policy gradient algorithm
1. 使用权重 $ {\theta ^Q} $和 ${\theta ^\mu }$随机初始化Q网络参数 $Q\left( {s,a\mid {\theta ^Q}} \right)$和策略
网络参数 $\mu \left( {s\mid {\theta ^\mu }} \right)$2. 使用初始化目标网络 3. 使用权重 ${\theta ^{Q'}} \leftarrow {\theta ^Q}$, ${\theta ^{\mu '}} \leftarrow {\theta ^\mu }$初始化目标网络 $Q'$和 $\mu '$ 4. 初始化经验池R 5. for episode=1, 2, ···, ${{M}}$，执行： 6. 为行动探索初始化一个随机过程 ${{N}}$ 7. 获得一个初始化观察状态 ${s_1}$ 8. for ${{t}} = 1,2,\cdots,T$，执行： 9. 根据当前策略与探索噪声选择行动 ${a_t}$ 10. 执行动作 ${a_t}$,获得奖励 ${r_t}$与新的状态 ${s_{t + 1}}$ 11. 将样本 $\left( {{s_t},{a_t},{r_t},{s_{t + 1}}} \right)$存储至经验池R 12. 从R中随机采样出N个样本 $\left( {{s_i},{a_i},{r_i},{s_{i + 1}}} \right)$ 13. 设置 ${y_i} = {r_i} + \gamma Q'\left( {{s_{i + 1}},\mu '\left( {{s_{i + 1}}\mid {\theta ^{\mu '}}} \right)\mid {\theta ^{Q'}}} \right)$ 14. 使用损失函数L更新Q网络参数 15. 使用采样样本的策略梯度更新行为策略 16. 更新目标网络参数： $ {\theta }^{{Q}^{\prime }}\leftarrow \tau {\theta }^{Q}+\left(1-\tau \right){\theta }^{{Q}^{\prime }} $ ${\theta ^{\mu '}} \leftarrow \tau {\theta ^\mu } + \left( {1 - \tau } \right){\theta ^{\mu '}}$ 17. end for 18. end for 
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表 1 雷达发射信号仿真参数表
Table 1. Radar transmit signal simulation parameters
参数类型 数值 信号类型 LFM 采样频率 ${f_{\rm{s}}}$ (MHz) 100 脉冲宽度 ${T_{\rm{p}}}$ (μs) 10 脉冲重复周期 ${T_{\rm{r}}}$ (μs) 50 下变频后的中频频率 ${f_I}$ (MHz) 25 调频斜率k (Hz/s) 2×1012 带宽B (MHz) 20
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表 2 3种干扰类型下的态势预测性能
Table 2. Posture prediction performance under 3 interference types
干扰类型 总体区间 步进 识别时间(ms) 识别精度(%) 噪声瞄准干扰 [3～4 GHz] 1 MHz 96 98.6 距离假目标欺骗干扰 [3～4 GHz] 1 MHz 132 97.4 密集假目标转发干扰 [1～1000 μs] 1 μs 144 94.4
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表 3 算法参数设置
Table 3. Algorithm parameters setting
参数 PPO TD3 DRL-ANCD Q网络学习率 10–3 10–3 10–3 策略网络学习率 10–3 10–3 10–3 优化器 Adam Adam Adam 目标网络更新率 10–3 5×10–3 5×10–3 批输入 128 128 128 折扣系数 0.99 0.99 0.99 奖励缩放 1.0 1.0 1.0 PPO裁剪参数 0.2 None None
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表 4 单一干扰类型下3种强化学习算法抗干扰性能
Table 4. Performance of 3 RL algorithms for a single jamming type
干扰类型 算法名称 平均奖励 决策时间(ms) 噪声瞄准干扰 PPO –215 188 TD3 –51 333 DRL-ANCD 53 244 距离假目标欺骗干扰 PPO –168 168 TD3 –25 225 DRL-ANCD 94 203 密集假目标转发干扰 PPO –156 269 TD3 –45 340 DRL-ANCD 24 289
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表 5 在线网络参数
Table 5. Online net parameters
网络 网络层 输入 输出 激活 策略网络 MLP1 State 256 ReLU MLP2 256 256 ReLU MLP3 256 128 ReLU MLP4 128 1 None Q网络 MLP1 State+action 256 ReLU MLP2 Action+256 256 ReLU MLP3 256 128 ReLU MLP4 128 1 None
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表 6 多干扰策略下3种强化学习算法抗干扰性能
Table 6. Performance of 3 RL algorithms for a multi-jamming strategies
干扰策略 算法名称 对抗奖励 决策时间(ms) 干扰策略Ⅰ PPO-SL –202 356 TD3-SL –125 443 DRL-ANCD 3 402 干扰策略Ⅱ PPO-SL –221 375 TD3-SL –122 429 DRL-ANCD 14 392 干扰策略Ⅲ PPO-SL –124 386 TD3-SL 25 463 DRL-ANCD 107 422
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