自动化学报2016，Vol.42Issue(9)：1322-1338,17.DOI:10.16383/j.aas.2016.c150829
基于直觉模糊集的时域证据组合方法研究
Combination of Temporal Evidence Sources Based on Intuitionistic Fuzzy Sets
摘要
Abstract
Evidence theory has been widely used in spatial and temporal information fusion. The sequential and dy-namic characteristics of temporal fusion calls for a new combination rule of temporal evidence sources. In this paper, temporal evidence combination is analyzed in the framework of evidence reliability and evidence discounting. A method of temporal evidence combination is proposed based on the composite reliability factor of temporal evidence. A ranking method for intuitionistic fuzzy values is firstly presented, followed by the presentation of evidence reliability evaluation based on intuitionistic fuzzy multiple criteria decision making. Then the relative reliability factors of evidence sources in neighboring time nodes are evaluated. By combining the relative reliability factor and real-time reliability factor yielded by the credibility decay model, a composite reliability factor is obtained. Finally, according to evidence discounting and Dempster0s combination rule, a method of temporal evidence combination based on the composite reliability factor is proposed. Numerical examples and simulation demonstrate that the proposed method is time sensitive, which can reflect the dynamic nature of temporal information fusion. Moreover, it is illustrated that this method can deal with conflict in temporal fusion well. The proposed temporal evidence combination rule can enhance the anti-interference performance of the target identification fusion system.关键词
证据理论/直觉模糊集/时域证据组合/可靠性评估/复合可靠度/证据折扣Key words
Evidence theory/intuitionistic fuzzy sets/temporal evidence combination/reliability evaluation/composite reliability factor/evidence discounting引用本文复制引用
宋亚飞,王晓丹,雷蕾..基于直觉模糊集的时域证据组合方法研究[J].自动化学报,2016,42(9):1322-1338,17.基金项目
国家自然科学基金(61273275,60975026,61503407,61573375)资助Supported by National Natural Science Foundation of China (61273275,60975026,61503407,61573375) （61273275,60975026,61503407,61573375）
