基于null定向天线和智能无人机蜂群行为的抗干扰
Anti-Jamming based on Null-Steering Antennas and Intelligent UAV Swarm Behavior
AI总结：
本文提出结合GA、SL和RL的统一优化框架，利用null定向天线和智能算法提升无人机蜂群在干扰下的通信稳定性与任务效率。
摘要：
无人机蜂群代表了自主系统的关键进展，通过无人机间的通信实现协同任务。然而，其对无线链路的依赖使其容易受到干扰，这可能会破坏协调和任务成功。本文研究了一种无人机蜂群是否能够在维持通信和任务效率的同时有效克服干扰。为此，提出了一种结合遗传算法（GA）、监督学习（SL）和强化学习（RL）的统一优化框架。任务模型被划分为时间槽和时期，允许动态路径规划、天线方向和蜂群编队，同时逐步执行碰撞规则。null定向天线通过将天线nulls指向干扰源来增强鲁棒性。结果显示，GA实现了稳定、无碰撞的轨迹，但计算成本高。SL模型复制了基于GA的配置，但在动态或受限设置下难以泛化。RL通过近端策略优化（PPO）训练，展示了适应性和实时决策能力，具有稳定的通信和较低的计算需求。此外，自适应运动模型通过基于旋转的机制将无人机运动泛化到任意方向，验证了所提系统的可扩展性。总体而言，配备null定向天线并由智能优化算法引导的无人机蜂群能够有效缓解干扰，同时保持通信稳定性、编队凝聚力和碰撞安全性。所提出的框架为未来研究鲁棒蜂群通信系统建立了统一、灵活和可重复的基础。
英文摘要：
Unmanned Aerial Vehicle (UAV) swarms represent a key advancement in autonomous systems, enabling coordinated missions through inter-UAV communication. However, their reliance on wireless links makes them vulnerable to jamming, which can disrupt coordination and mission success. This work investigates whether a UAV swarm can effectively overcome jamming while maintaining communication and mission efficiency. To address this, a unified optimization framework combining Genetic Algorithms (GA), Supervised Learning (SL), and Reinforcement Learning (RL) is proposed. The mission model, structured into epochs and timeslots, allows dynamic path planning, antenna orientation, and swarm formation while progressively enforcing collision rules. Null-steering antennas enhance resilience by directing antenna nulls toward interference sources. Results show that the GA achieved stable, collision-free trajectories but with high computational cost. SL models replicated GA-based configurations but struggled to generalize under dynamic or constrained settings. RL, trained via Proximal Policy Optimization (PPO), demonstrated adaptability and real-time decision-making with consistent communication and lower computational demand. Additionally, the Adaptive Movement Model generalized UAV motion to arbitrary directions through a rotation-based mechanism, validating the scalability of the proposed system. Overall, UAV swarms equipped with null-steering antennas and guided by intelligent optimization algorithms effectively mitigate jamming while maintaining communication stability, formation cohesion, and collision safety. The proposed framework establishes a unified, flexible, and reproducible basis for future research on resilient swarm communication systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
