基于直觉模糊熵权和CC-OWA算子的雷达目标识别模型

为更完整的描述和表达雷达目标类型识别中的目标特征和目标类型之间的关系复杂性和知识缺乏性,通过直觉模糊关系描述,进而将目标识别特征信息转化为直觉模糊集信息.分析了基于直觉模糊集理论的雷达目标类型识别知识建模,揭示了直觉模糊信息的价值可以通过直觉模糊熵刻画,进而提出应用直觉模糊集的熵构造特征直觉模糊信息的权重(直觉模糊熵权),充分利用了目标类型识别知识中隐含的权重信息,并结合CC-OWA算子建立雷达目标类型识别模型与识别步骤,利用一个雷达目标识别实例说明了模型的有效性.

Radar Target Recognition Model Based on Entropy of Intuitionistic Fuzzy Sets and CC-OWA Operator

In order to comprehensively describe and articulate the relationship between features and types of targets in the target type recognition (TTR) problem, intuitionistic fuzzy relation is adopted explicitly. Feature information of target can be transformed into intuitionistic fuzzy information represented by intuitionistic fuzzy sets or its corresponding interval value sets. Through analysis on modeling TTR knowledge based on intuitionistic fuzzy sets, the fact that the value of intuitionistic fuzzy information can be measured by the entropy of intuitionistic fuzzy sets is revealed and the method of formulating weights by the entropy of intuitionistic fuzzy sets (called intuitionistic fuzzy entropy weight or IFEW) is proposed as well. The weight formation makes good use of information underlying knowledge of TTR represented by intuitionistic fuzzy relation. The combined continuous interval argument ordered weighted averaging (CC-OWA) operator is incorporated with IFEW into radar TTR model and steps. Finally, an example is presented to demonstrate the effectiveness of the proposed method and model.