一个基于DQN的分布式智能反干扰方法,用于无人机
Dadong Ni1, Shuo Ma2, Junyi Du1
1National Key Laboratory of Complex Aviation System Simulation, Chengdu 610036, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
这项研究引入了一个联合学习层次深度Q网络 (FL-HDQN),用于在无人机群中进行合作的反干扰. 该方法提高了决策准确性和隐私性,同时减少了智能防干扰系统的通信开销.
科学领域:
- 智能通信系统 智能通信系统
- 在无人机中深度学习应用.
- 对于无人机群体的合作决策.
背景情况:
- 深度学习反干扰对于无人机系统至关重要.
- 单个无人机模型面临着数据隔离和群体中的决策不一致.
- 在群体中共享数据增加了通信开销和安全风险.
研究的目的:
- 提出一种新的多无人机合作智能反干扰决策方法.
- 解决数据孤立,决策不一致,通信开销和安全挑战.
- 提高无人机群中的反干扰策略的有效性和效率.
主要方法:
- 联合学习-层次深度Q网络 (FL-HDQN) 框架.
- 使用本地参数进行协作全球模型培训的自适应模型同步.
- 层次深度强化学习用于多域优化 (时间-频率,功率,调制-编码).
主要成果:
- 通过共享模型参数而不是原始数据,FL-HDQN确保了决策的一致性,并保护了数据隐私.
- 层次模型有效地将复杂的优化解为可管理的层.
- 与最先进的智能反干扰模型相比,实现了1%更高的决策准确性.
结论:
- 拟议的FL-HDQN方法为无人机群中的合作智能反干扰提供了优越和有效的解决方案.
- 联合学习显著降低了通信成本,并提高了数据安全性.
- 层次深度强化学习提高了复杂干扰环境中的决策性能.
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