国防科技大学学报2026，Vol.48Issue(4)：43-54,12.DOI:10.11887/j.issn.1001-2486.25050016
卷积时序融合网络在无人机集群频谱优化中的应用
Application of convolutional temporal fusion networks in spectrum optimization for UAV swarms
摘要
Abstract
This study proposed a spectrum resource optimization algorithm for UAV swarms under dynamically changing communication tasks and interference environments,based on a convolutional temporal fusion network.Specifically,the study leveraged the local feature extraction capability of convolutional neural networks and the temporal modeling ability of long short-term memory networks to enhance the autonomous learning and adaptability of UAV swarms.By integrating double deep Q-network within a multi-agent framework,distributed online training was performed,enabling each UAV in the swarm to respond quickly to dynamic tasks and interference while optimizing spectrum resources based solely on local observations.Simulation results show that,in environments with dynamic communication tasks and interference,the proposed algorithm outperforms conventional methods,not only improving spectrum resource utilization efficiency but also demonstrating excellent stability.关键词
无人机集群/频谱资源优化/卷积时序融合网络/通信任务动态变化Key words
UAV swarm/spectrum resource optimization/convolutional temporal fusion network/dynamic changes in communication tasks分类
信息技术与安全科学引用本文复制引用
陈勇,娄登科,陈泳,钱鹏智,杜奕航..卷积时序融合网络在无人机集群频谱优化中的应用[J].国防科技大学学报,2026,48(4):43-54,12.基金项目
国防科技大学自主基金资助项目(22-ZZCX-059) （22-ZZCX-059）
