Abstract: 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.

Key words: target threat assessment, index correlation, improved particle swarm optimization, extreme learning machines, principal component analysis