摘要
通过对目标高度、距离、速度、角度这些空间态势因素和空战能力因素的分析,建立了目标威胁评估模型,提出了基于PSO-BP(粒子群和后向传播)算法的目标威胁程度评估方法。通过对空中八个目标某一时刻威胁程度的预测,并将结果与多数属性决策方法的结果进行了比较,表明此方法有效地解决了空战目标威胁评估问题,大大提高了决策的客观性。
This paper established a target thread model based on the factors of space situation and air combat capacity of targets, such as altitude, distance, speed and angle, and proposed a target threat level assessment method based on PSO （particle swarm optimization） and BP（back-propagation） algorithm to estimate the threat level of aerial targets. Through predicting threat level of 8 aerial targets, the result shows that this method is effective to solve the problem of air threat assessment target, greatly improving the objectivity of decision-making.
出处
《计算机应用研究》
CSCD
北大核心
2012年第3期900-901,932,共3页
Application Research of Computers
基金
航空科学基金资助项目(20090580013)
中央高校基础研究基金资助项目(ZYGX2009J092)
关键词
BP神经网络
粒子群算法
威胁指数法
威胁估计
BP neural network
particle swarm optimization algorithm
threat index method
threat assessment
