Intelligent Decision-Making Algorithm for Multi-UAV Radar Cooperative Guided Search Task Based on Multi-Agent Reinforcement Learning
Li, Xiaoyang Wang, Teng Wang, Yongkun Li, Yang Alfarraj, Osama Guizani, Mohsen
Li, Xiaoyang
Wang, Teng
Wang, Yongkun
Li, Yang
Alfarraj, Osama
Guizani, Mohsen
Supervisor
Department
Machine Learning
Embargo End Date
Type
Journal article
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
To address the multi-UAV radar cooperative guided search task in the scenarios of large airspace with widespread distribution of cluster targets, an autonomous hierarchical decision-making framework based on multi-agent reinforcement learning is proposed to guide different airborne radar search processes in cooperative search tasks. Firstly, the top-level and bottom-level strategy modules are constructed based on the cooperative airspace set-covering model and search performance optimization models established in the above scenario respectively. Secondly, a cooperative search environment based on real-time beam scheduling is constructed where a complementary scheduling strategy for cooperative radar search beam positions is proposed to minimize cooperative search time and maximize global radar search performance. Finally, the lamaddpg-rgs algorithm is proposed to perform long-short term memory (LSTM) and feature extraction on the variable global observation state sequence, aiming to improve the algorithm training effect and convergence speed. Simulation results show that the trained hierarchical decision-making multi-agents can make precise autonomous decisions rapidly based on local observation states and current search task process. The optimization effect of radar cooperative search performance in above scenario based on proposed algorithm is superior to traditional algorithms.
Citation
X. Li, T. Wang, Y. Wang, Y. Li, O. Alfarraj and M. Guizani, "Intelligent Decision-Making Algorithm for Multi-UAV Radar Cooperative Guided Search Task Based on Multi-Agent Reinforcement Learning," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2025.3572395
Source
IEEE Internet of Things Journal
Conference
Keywords
Multi-UAV, Reinforcement learning, Phased arrays, Radar detection, LSTM
Subjects
Source
Publisher
IEEE