摘要
无人作战飞机作战态势变化多样,地面指挥人员对机载雷达操控困难。针对不同作战态势下雷达工作模式选择需求不同,为了提高雷达的探测精度,提出了一种基于模糊决策树的无人作战飞机雷达模式管理算法。算法利用专家知识,以敌方目标和模式选择结果的经验数据为训练样本,以作战过程中的实时目标为测试数据。通过样本的模糊信息生成树的属性节点和分支,对测试数据进行推理分类,分类结果以信任度表示,为实时的雷达模式管理方式,并进行空战想定下的仿真试验。结果表明,各模式信任度的取值变化与空战中雷达各模式的需求程度变化相吻合,提高雷达探测精确性,实现无人作战飞机雷达智能管理的要求。
The actual requirements of unmanned combat air vehicle （UCAV） airborne radar mode varied in different situations of an air combat. Radar mode management technology can reduce the difficulty of selecting a radar mode for ground commander. In this paper, an airborne radar mode management algorithm based on Fuzzy Decision Tree（FDT） is proposed. By utilizing expert knowledge, FDT learning is processed by using a set of training data including air targets data and mode selecting instances. FDT reasoning is made aiming at classifying a real time air target data. The classification consists of membership values of different modes. An air combat scenario simulation is carried out. The simulation results indicate that the changing membership value of each mode is coincident with the actual requirements of mode management and the algorithm has directive effects on implementing UCAV radar intelligent management.
出处
《计算机仿真》
CSCD
北大核心
2011年第3期65-68,129,共5页
Computer Simulation
基金
西北工业大学研究生创业种子基金资助(Z200947)
关键词
无人作战飞机
模糊决策树
雷达模式管理
Unmanned combat air vehicle（UCAV）
Fuzzy decision tree（FDT）
Radar mode management
