摘要
在增强无人机战斗力的研究中,针对无人作战飞机(UCAV)对地多目标序次攻击决策问题展开研究,建立多目标序次攻击的决策模型。为提高UCAV执行多目标攻击任务的成功率,决策模型综合考虑了UCAV在整个任务过程中所面临的战场风险,并将决策算法与航路规划问题分解,提出一种基于威胁分布密度的飞机生存概率评估方法,提高了决策效率。将序次决策问题转化为最优搜索问题,利用自适应蚁群算法求解决策问题并给出具体算法流程。仿真结果表明,多目标序次攻击决策方法能有效解决复杂战场环境分布所带来的目标排序优化问题,给出合理的多目标攻击时序,为方案设计提供了依据。
Aimed at the UCAV decision problem in air-to-ground multi-target sequential attack,founded a decision model of it.In order to enhance the success rate in executing multi-target attacking mission,this integrative model concerned the battle field risk during the period of executing mission of the UCAV,considered decision algorithms and air route planning separately,given a survival rate estimation method based on the distribution density of threatens,improved the efficiency of decision.Using Ant Colony Algorithm in solving the decision problem converted the decision problem to optimal searching problem,given the flow of the algorithm.The simulation result shows that this targets sequence optimize problem caused by complex battle field environment distribution can be solved by the method of multi-target sequential attacking efficaciously,and can give a reasonable multi-target attacking sequence in time.
出处
《计算机仿真》
CSCD
北大核心
2010年第12期51-54,78,共5页
Computer Simulation
