This study aims to automate threat assessment and target assignment processes in air defense systems using a dynamic, learning artificial intelligence-based model. Unlike threat assessment studies in the literature that use different criteria and methods, this study integrates missing data completion, multi-criteria analysis, and artificial neural networks to dynamically update the threat score. Furthermore, unlike studies in the literature, the number of criteria used has been increased to enable the model to provide a broader perspective. Most studies are static and use a small number of criteria; this study presents a dynamic, multi-criteria model that can handle incomplete data. The developed Geometric Threat Score proposes an average perspective for threat assessment, which varies depending on individuals and geographical conditions. The model generates threat scores using criterion data obtained from radars and sensors and can respond adaptively to changing conditions. The results achieved demonstrated high performance with mean square errors (MSE) of 0.0005–0.0072 and a correlation coefficient (R) above 95%. This approach accelerates decision support processes in air defense systems, reducing human influence and increasing system effectiveness.
Taşdemir et al. (Thu,) studied this question.