基于机器学习的空袭重点目标挖掘

Air Attack Target Mining Based on Machine Learning

精准挖掘空袭重要目标是克敌制胜的基础。提出了一种基于机器学习的空袭重点目标挖掘方法。首先,针对空袭重点目标挖掘,构建了空袭目标网络体系,并分析了影响目标网络体系能力的因素;然后,在此基础上构建了目标价值模型,用采集的目标价值相关数据集训练获得基于随机森林/神经网络的目标价值评分模型,根据目标评分高低得到重点目标清单;最后,仿真试验结果表明,该方法可为空袭作战提供可靠支撑。

Accurately mining air attack targets is the foundation of the victory.An air attack target mining method based on machine learning is proposed.Firstly,aimed at the air attack target mining,an air attack target network system is constructed,and the factors affecting the capability of the target network system are analyzed.Then,based on the above,a target value model is constructed.After the training with the related dataset of the target value,a target value scoring model is obtained based on the random forest/neural network.According to the target scorings,a list of the air attack target is obtained.Finally,simulation experiment result shows that the method can provide reliable support to the air attack operation.