Jiang Wen, Wang Shiyu, Liu Xiang, Zheng Hanqing, Wei Boya
School of Electronics and Information, Northwestern Polytechnical University, Xi'an, Shaanxi Province, 710072, China.
Infrared Detection Technology Research & Development Center, Shanghai Institute of Spaceflight Control Technology, CASC, Shanghai 200233, China.
PLoS One. 2017 May 18;12(5):e0177828. doi: 10.1371/journal.pone.0177828. eCollection 2017.
Dempster-Shafer evidence theory has been extensively used in many information fusion systems since it was proposed by Dempster and extended by Shafer. Many scholars have been conducted on conflict management of Dempster-Shafer evidence theory in past decades. However, how to determine a potent parameter to measure evidence conflict, when the given environment is in an open world, namely the frame of discernment is incomplete, is still an open issue. In this paper, a new method which combines generalized conflict coefficient, generalized evidence distance, and generalized interval correlation coefficient based on ordered weighted averaging (OWA) operator, to measure the conflict of evidence is presented. Through ordered weighted average of these three parameters, the combinatorial coefficient can still measure the conflict effectively when one or two parameters are not valid. Several numerical examples demonstrate the effectiveness of the proposed method.
自邓普斯特（Dempster）提出并由谢弗（Shafer）扩展以来，邓普斯特 - 谢弗证据理论已在许多信息融合系统中得到广泛应用。在过去几十年里，许多学者对邓普斯特 - 谢弗证据理论的冲突管理进行了研究。然而，当给定环境处于开放世界，即识别框架不完整时，如何确定一个有效的参数来衡量证据冲突仍然是一个未解决的问题。本文提出了一种基于有序加权平均（OWA）算子的新方法，该方法结合广义冲突系数、广义证据距离和广义区间相关系数来衡量证据冲突。通过对这三个参数进行有序加权平均，当其中一两个参数无效时，组合系数仍能有效地衡量冲突。几个数值例子证明了所提方法的有效性。
