用无人机群强化学习提升雷达感知精度
Reinforcement Learning for Enhancing Sensing Estimation in Bistatic ISAC Systems with UAV Swarms
- 多智能体强化学习优化无人机轨迹与定位
- 显著提升复杂环境下的感知性能
- 适合智能雷达与通信系统研究者
本文提出一种新型多智能体强化学习（MARL）框架，用于增强基于无人机群的双基地集成感知与通信（ISAC）网络。将无人机的定位与轨迹优化建模为部分可观测马尔可夫决策过程，采用集中训练、分散执行的MARL方法，以最大化整体感知性能。具体地，设计了一种去中心化协作策略，使无人机能自主发展有效通信协议，提升环境感知与运行效率。同时引入发射功率自适应技术，缓解无人机间通信干扰，优化协议效率。尽管复杂度上升，该方案在多种场景下仍表现出强鲁棒性与适应性，为未来ISAC网络提供可扩展、低成本的增强方案。
原文摘要 · Abstract (English)
This paper introduces a novel Multi-Agent Reinforcement Learning (MARL) framework to enhance integrated sensing and communication (ISAC) networks using unmanned aerial vehicle (UAV) swarms as sensing radars. By framing the positioning and trajectory optimization of UAVs as a Partially Observable Markov Decision Process, we develop a MARL approach that leverages centralized training with decentralized execution to maximize the overall sensing performance. Specifically, we implement a decentralized cooperative MARL strategy to enable UAVs to develop effective communication protocols, therefore enhancing their environmental awareness and operational efficiency. Additionally, we augment the MARL solution with a transmission power adaptation technique to mitigate interference between the communicating drones and optimize the communication protocol efficiency. Moreover, a transmission power adaptation technique is incorporated to mitigate interference and optimize the learned communication protocol efficiency. Despite the increased complexity, our solution demonstrates robust performance and adaptability across various scenarios, providing a scalable and cost-effective enhancement for future ISAC networks.
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