基于结构熵和PSO-RBF的空战动态威胁评估

陈洁钰; 姚佩阳; 税冬东; 赵雪岩

doi:doi:10.3969/j.issn.1671-637x.2014.11.010

电光与控制, 2014, 21 (11): 51, 网络出版: 2014-12-08

基于结构熵和PSO-RBF的空战动态威胁评估

Air Combat Dynamic Threat Assessment Based on Structure Entropy and PSO-RBF

论文大纲

陈洁钰姚佩阳税冬东赵雪岩

作者单位

空军工程大学信息与导航学院，西安 710077

摘要

针对传统威胁评估方法不能很好地体现空战过程中各类威胁因素作用变化的问题，引入了径向基神经网络，采用结构熵权法优化了神经网络训练参数，提出了基于粒子群和径向基神经网络(PSO-RBF)算法的空战动态权值计算方法。以某一时刻预测多无人机空中对抗时的威胁度为想定，分别采用结构熵权法和PSO-RBF算法进行仿真计算。仿真结果表明所提方法可有效解决空战目标威胁评估问题，提高了决策的客观性、科学性。

Abstract

Since the traditional threat assessment methods can’t reflect the changing of threat factors during air combat,a dynamic weight calculating method was proposed based on particle swarm and radical basis function neural network (PSO-RBF) by introducing RBF neural network and using a new structure entropy weight method to optimize the training parameters of RBF neural network.On the circumstance of assessing the threat degree during multi-UCAV cooperative combat,simulations were executed by using structure entropy weight method and PSO-RBF method respectively.The result proved that the PSO-RBF process can assess the threat degree of the target during air combat effectively and make the strategic decision more objective and reasonable.

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陈洁钰, 姚佩阳, 税冬东, 赵雪岩. 基于结构熵和PSO-RBF的空战动态威胁评估[J]. 电光与控制, 2014, 21(11): 51. CHEN Jie-yu, YAO Pei-yang, SHUI Dong-dong, ZHAO Xue-yan. Air Combat Dynamic Threat Assessment Based on Structure Entropy and PSO-RBF[J]. Electronics Optics & Control, 2014, 21(11): 51.

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