A Multi-Target Threat Assessment Method Based on Objective Three-Way Decision

• Published in: IEEE access Vol. 13; pp. 681 - 694
• Main Authors: Pires, Humberto Baldessarini, Guimaraes, Lamartine Nogueira Frutuoso, Reboucas, Sergio
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Avoidance; Classification; Computational modeling; Correlation; Decision making; Decision-making process; Effectiveness; Fuzzy logic; Fuzzy set theory; Fuzzy sets; intuitionistic fuzzy numbers; Parameter uncertainty; risk avoidance coefficient; Risk perception; Rough sets; Terminology; Threat assessment; three-way decision; TOPSIS method; Uncertainty; Uncertainty analysis; Wearable Health Monitoring Systems; weighted heronian mean operator
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3523817

Real-world applications of threat assessment (TA) require enhanced timeliness, which can be achieved by reducing reliance on subject matter experts. However, subjective procedures remain prevalent in the literature, particularly within decision methods and three-way decision (3WD) approaches. Traditional 3WD often utilizes a subjectively defined, fixed risk avoidance coefficient that reflects human perception of battlefield information. This study addresses these challenges by proposing a dynamic multi-target TA method within an intuitionistic fuzzy environment, eliminating subjective parameter settings in the technique for order of preference by similarity to ideal solution (TOPSIS), and incorporating an objective 3WD (O3WD) approach. The O3WD quantifies uncertainty through cosine entropy, modeling its relationship with risk avoidance coefficients non-linearly. Given the complex interplay of factors-such as sensor accuracy and environmental conditions-that contribute unpredictably to uncertainty, this concept better captures the dynamics inherent in TA. Two case studies were conducted to evaluate the O3WD's effectiveness in target categorization across a range of <inline-formula> <tex-math notation="LaTeX">\gamma </tex-math></inline-formula> values, a parameter that controls the curvature of the risk function. The first case showed that the proposed method excelled in recent TA methods in threat ranking. An objective analysis of attribute uncertainties revealed that the compared methods applied overly high-risk avoidance coefficients, leading to inaccurate decisions, a finding also supported by the second case. The proposed method enables precise categorization based on attribute uncertainty without parameter adjustments, highlighting its effectiveness and suitability for multi-target TA in real-world scenarios.