Dynamic Multi-Target Three-Way Threat Assessment in the Context of Air Defense

Multi-target threat assessment is vital in information warfare, as it significantly improves decision making. This importance is amplified when threat assessment is applied through a three-way decision framework that categorizes threats into distinct decision types and dynamically considers information from multiple time points. However, the existing literature on air defense often highlights the limitation of conducting a multi-target threat assessment focused only on a single defended asset. To address this gap, this study introduces two innovative methods for dynamic multi-target three-way threat assessment concerning multiple defended assets. Both methods calculate attribute weights using cosine intuitionistic fuzzy entropy and a nonlinear programming model, along with an extended version of the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution). The first method employs an advanced form derived from the Heronian Mean operator to aggregate multi-target data, and utilizes the extended TOPSIS for ranking targets. In contrast, the second method exclusively applies the extended TOPSIS to individually assess multi-target information for each defended asset and establish comprehensive target threat values. The case study demonstrated that the extended TOPSIS, when compared to a recently introduced decision method in the literature, was able to effectively manage multiple targets, with both proposed methods maintaining consistent threat rankings. Additionally, the first proposed method exhibited improved capability in distinguishing high-priority targets, whereas the second method proved more successful in discriminating targets of intermediate importance. These findings suggest that both methods are valuable and can significantly enhance the performance of dynamic multi-target three-way threat assessment in the realm of air defense.