基于在线支持向量机的空对地攻击决策算法

为解决无人机对地攻击决策问题, 对影响地面目标威胁度的指标因素进行了分析和量化, 构建了基于在线支持向量的目标威胁度预测模型。利用在线支持向量机实现目标威胁度排序, 进而完成空对地的攻击决策。研究的空对地决策算法具有在线训练、 模型精确度高、 需要样本少和泛化能力强等特点, 有利于快速准确地进行空对地攻击决策。最后, 通过仿真实例验证该算法的正确性。仿真结果表明, 在线支持向量机在计算目标威胁度过程中速度快且精确度高。

Air-to-Ground Attack Decision-Making Technology Based on Online Support Vector Machine

In order to solve the problem of the decision-making for the air-to-ground attack, the index factors of threaten degree of the target are analyzed and quantified. And a model of online support vector machine is built for threaten degree of the target. On the basis of above analysis, the attack decision is completed by sorting the threaten degree of the target. The developed attack decision-making algorithm using online support vector machine has features such as online training, small amount samples and good generalization ability. The simulation example is given to show the effectiveness of the proposed decision-making for the air-to-ground attack. The simulation results show that the online support vector machine has quick training ability and high-accuracy in the process of threaten degree of the target calculation.