雷达
计算机科学
运动规划
运动学
路径(计算)
强化学习
弹道
钢筋
跟踪(教育)
人工智能
数学优化
工程类
数学
电信
天文
程序设计语言
物理
机器人
心理学
经典力学
结构工程
教育学
作者
R.U. Hameed,Adnan Maqsood,Ali Hashmi,Tahir Saeed,Rizwan Riaz
出处
期刊:Journal of the Royal Aeronautical Society
[Cambridge University Press]
日期:2021-10-26
卷期号:126 (1297): 547-564
被引量:5
摘要
Abstract This paper discusses the utilisation of deep reinforcement learning algorithms to obtain optimal paths for an aircraft to avoid or minimise radar detection and tracking. A modular approach is adopted to formulate the problem, including the aircraft kinematics model, aircraft radar cross-section model and radar tracking model. A virtual environment is designed for single and multiple radar cases to obtain optimal paths. The optimal trajectories are generated through deep reinforcement learning in this study. Specifically, three algorithms, namely deep deterministic policy gradient, trust region policy optimisation and proximal policy optimisation, are used to find optimal paths for five test cases. The comparison is carried out based on six performance indicators. The investigation proves the importance of these reinforcement learning algorithms in optimal path planning. The results indicate that the proximal policy optimisation approach performed better for optimal paths in general.
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