群体行为
计算机科学
跟踪(教育)
避碰
软件部署
弹道
粒子群优化
实时计算
碰撞
人工智能
算法
天文
计算机安全
操作系统
物理
教育学
心理学
作者
Longyu Zhou,Supeng Leng,Qiang Liu,Qing Wang
出处
期刊:IEEE Internet of Things Journal
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:9 (1): 743-754
被引量:41
标识
DOI:10.1109/jiot.2021.3085673
摘要
With the advantages of easy deployment and flexible usage, unmanned aerial vehicle (UAV) has advanced the multitarget tracking (MTT) applications. The UAV-MTT system has great potentials to execute dull, dangerous, and critical missions for frontier defense and security. A key challenge in UAV-MTT is how to coordinate multiple UAVs to track diverse invading targets accurately and consecutively. In this article, we propose a UAV swarm-based cooperative tracking architecture to systematically improve the UAV tracking performance. We design an intelligent UAV swarm-based cooperative algorithm for consecutive target tracking and physical collision avoidance. Moreover, we design an efficient cooperative algorithm to predict the trajectory of invading targets accurately. Our simulation results demonstrate that the swarm behaviors stay stable in realistic scenarios with perturbing obstacles. Compared with state-of-the-art solutions, such as the matched deep $Q$ -network, our algorithms can increase tracking accuracy by 60%, reduce tracking delay by 23%, and achieve physical collision-avoidance during the tracking process.
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