电光与控制, 2013, 20 (1): 77, 网络出版: 2013-01-24
基于人工势场启发粒子群算法的空战机动决策
Decision-Making of Air Combat Maneuvering Based on APF and PSO
空战 机动决策 人工势场 粒子群算法 变权重 Artificial Potential Field(APF) Particle Swarm Optimization(PSO) maneuvering decision-making variable weight
摘要
以敌我双机空战为背景,基于滚动时域控制思想对空战机动决策进行研究。借鉴人工势场的思想构建了空战人工势场,重点分析了人工势场函数的构建,变权重函数的建立,提出了人工势场启发粒子群算法的空战机动决策方法,最后进行仿真验证。仿真结果表明,该方法能有效消除人工势场的局部极小值问题,同时也改善了粒子群算法全局搜索能力,避免了早熟,得到的结果是有效的。
Abstract
In order to solve the complicated problem of decision-making in modern air combat a decision-making model based on receding horizon control was established considering a scenario of air combat involving two opposed fighters.Adopting the thought of Artificial Potential Field (APF) the air combat APF was built up and the establishment of artificial potential function and weight function was studied.A decision-making method for air combat maneuvering based on APF and Particle Swarm Optimization (PSO) was proposed.Simulation results show that this algorithm can avoid the trap of local minimum of APF improve the global optimization ability of PSO and can also prevent early maturity.
张涛, 于雷, 周中良, 李飞. 基于人工势场启发粒子群算法的空战机动决策[J]. 电光与控制, 2013, 20(1): 77. ZHANG Tao, YU Lei, ZHOU Zhongliang, LI Fei. Decision-Making of Air Combat Maneuvering Based on APF and PSO[J]. Electronics Optics & Control, 2013, 20(1): 77.