基于PCA-MPSO-ELM的空战目标威胁评估

目标威胁评估是空战对抗过程中的关键环节。由于影响空战目标威胁评估的因素复杂多样，且指标之间存在相关性，导致传统的评估算法无法得到准确客观的评估结果。由此，提出了一种基于主成分分析法和改进粒子群算法优化的极限学习机（PCA-MPSO-ELM）的目标威胁评估算法。首先，综合分析了影响目标威胁程度的指标，利用主成分分析法对原始评估指标进行线性变化处理得到综合变量，消除了评估指标之间的相关性，实现了对评估数据的降维；在此基础上，构建ELM神经网络并利用改进的粒子群算法优化极限学习机的输入权值和阈值，提高了目标威胁评估模型的精度。最后，在空战训练测量仪中选取空战对抗数据，利用威胁指数法构造了目标威胁评估样本数据，通过仿真实验分析了PCA-MPSO-ELM算法的精度和实时性，结果表明所提算法可以快速准确地进行空战目标威胁评估。

Target threat assessment in air combat based on PCA-MPSO-ELM algorithm

Target threat assessment is a key link in air combat. Due to the complex and diverse factors affecting the threat assessment of air combat targets and the correlation between the indicators, traditional assessment algorithm cannot obtain accurate and objective assessment results. This paper proposes a target threat assessment algorithm based on a Principal Component Analysis method and an Modified Particle Swarm Algorithm Optimized for Extreme Learning Machines (PCA-MPSO-ELM). Indicators affecting the degree of target threat values were comprehensively analyzed first, followed by linear changes in the original evaluation indicators using the principal component analysis method to obtain comprehensive variables, eliminating the correlation between the evaluation indicators and achieving dimensionality reduction of the evaluation data. On the basis of data pretreatment, the ELM neural network was established and the improved particle swarm algorithm was applied to the optimization of the input weights and threshold values of ELM to improve the accuracy of the target threat assessment model. Finally, air combat data was selected from the air combat maneuvering instrument, and sample data for target threat assessment was constructed using the threat index method. The accuracy analysis and real-time analysis of the assessment were carried out in simulation experiments, and the results showed that the proposed algorithm can achieve accurate and rapid target threat assessment in air combat.