Abstract
Threat assessment for aerial attack targets is an important aspect of air defense weapon systems in responding to multiple attacks. We establish a model based on intuitionistic fuzzy rough sets (IFRS) and D-S evidence theory for threat assessment from the required data with uncertainty in stages. Using the overall degree of dependency and attribute importance of the intuitionistic fuzzy information system as a heuristic function, we study algorithms to extract threat elements and rules based on IFRS, to generate an intuitionistic fuzzy rule base for threat assessment with degrees of belief and disbelief. Based on the threat assessment rule base, we study the BPA determination algorithm in multi-stage threat assessment. The intuitionistic fuzzy semantics of degree of belief in the rule conclusion are used to determine the focal elements corresponding to each aggregate rule, and to obtain the degree of support of the data in a stage for each threat level. A case study shows that, compared to a threat assessment method based solely on D-S evidence theory or intuitionistic fuzzy reasoning, the advantage of IFRS knowledge acquisition makes the selection of threat assessment elements and determination of BPA more objective and less dependent on domain experts, so as to yield strong, objective results.
Highlights
Threat assessment (TA) is at the third level of the JDL information fusion model
We established a threat assessment model based on intuitionistic fuzzy rough sets (IFRS) and D-S evidence theory to deal with the threat assessment from multi-stage data with uncertainty caused by the battlefield environment and detection equipment
We studied a threat element extraction algorithm based on IFRS
Summary
Threat assessment (TA) is at the third level of the JDL information fusion model. It quantifies the ability of an enemy’s military deployment or weaponry to pose a threat, along with the enemy’s possible action intention. The battlefield situation and threat assessment processes data at the decision-making level, and solves a specific domain problem based on the commander’s battlefield knowledge and combat experience. Data analysis and automatic knowledge acquisition based on RS and IFRS have attracted much attention in the field of intelligent decision-making, providing new ideas and methods for the extraction of threat assessment factors and knowledge discovery in uncertain environments. Based on the degree of dependency and attribute importance, we can define the relative reduction of the information system. The overall degree of dependency before and after the reduction of the intuitionistic fuzzy information system remains unchanged. The reduction with the lowest dimensionality is referred to as the minimum reduction
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