TY - JOUR
T1 - Intelligent decision-making algorithm for airborne phased array radar search tasks based on a hierarchical strategy framework
AU - LI, Xiaoyang
AU - WANG, Teng
AU - WANG, Dinghan
AU - ZHANG, Hairuo
AU - ZHOU, Ying
AU - ZHOU, Deyun
N1 - Publisher Copyright:
© 2024
PY - 2024/11
Y1 - 2024/11
N2 - To address the guided search task of airborne phased array radar in the scenarios of large airspace with widespread distribution of cluster targets in Beyond Visual Range (BVR) air combat, a hierarchical strategy framework based on deep reinforcement learning is proposed to guide different stages of search tasks. Firstly, an airspace set-covering model and a radar parameter optimization model for the guided search task of cluster targets are established. Secondly, the hierarchical strategy framework including upper-level and lower-level strategies is constructed based on the above models. Finally, the happo-rgs algorithm is proposed for feature extraction from Markov continuous observation sequences, to enhance the training effectiveness and improve the algorithm convergence speed. Simulation results show that the trained agent can make precise autonomous decisions rapidly based on airspace-target covering situation and target guidance information which significantly improves the radar search performance in the forementioned scenarios compared to traditional algorithms.
AB - To address the guided search task of airborne phased array radar in the scenarios of large airspace with widespread distribution of cluster targets in Beyond Visual Range (BVR) air combat, a hierarchical strategy framework based on deep reinforcement learning is proposed to guide different stages of search tasks. Firstly, an airspace set-covering model and a radar parameter optimization model for the guided search task of cluster targets are established. Secondly, the hierarchical strategy framework including upper-level and lower-level strategies is constructed based on the above models. Finally, the happo-rgs algorithm is proposed for feature extraction from Markov continuous observation sequences, to enhance the training effectiveness and improve the algorithm convergence speed. Simulation results show that the trained agent can make precise autonomous decisions rapidly based on airspace-target covering situation and target guidance information which significantly improves the radar search performance in the forementioned scenarios compared to traditional algorithms.
KW - Beyond-visual-range air combat
KW - Multi-head attention mechanism
KW - Phased array radar
KW - Radar search resource optimization
KW - Reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85204735624&partnerID=8YFLogxK
U2 - 10.1016/j.cja.2024.09.006
DO - 10.1016/j.cja.2024.09.006
M3 - 文章
AN - SCOPUS:85204735624
SN - 1000-9361
VL - 37
SP - 398
EP - 419
JO - Chinese Journal of Aeronautics
JF - Chinese Journal of Aeronautics
IS - 11
ER -