摘要
概述了多传感器探测云环境下分布式信息融合系统测量数据一致性判定与优化选择融合处理的基本思想,并指出了Luo方法在数据一致性度量、关联判定、参与融合处理数据选择以及方法适应性等方面的不足。阐述了基于稳健统计理论、基于模糊理论、基于特征值和基于统计置信距离等一致性信息融合方法对不同传感器的测量数据进行一致性测度和判定的思想。不同解决方案均不同程度上改进了Luo方法,但信息源质量影响和融合策略选择等方面的问题仍未解决,需进一步研究。
The basic ideas of the measuring data consensus and the optimization selection fusion processing of the distributed information fusion system in the multi-sensor detection cloud envi ronment are summarized. The shortcomings of Luo＇s method in the data consistency metrics, the associated determination, the fusion processing data selection, and the method adaptability are pointed out. The consensus measure and discriminant methods based on the robust statistical the ory, the fuzzy theory, the matrix eigenvalue, and the statistical confidence distance are intro duced. Different solutions can improve Luo＇s method from different perspectives. But the effects on information source quality and fusion strategies are still unsolved, thus it is the direction for the following studies.
作者
熊朝华
刁联旺
张永伟
吴蔚
XIONG Zhaohua;DIAO Lianwang;ZHANG Yongwei;WU Wei(Science and Technology on Information Systems Engineering Laboratory, Nanjing 210007 ,China;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210009, China)
出处
《指挥信息系统与技术》
2018年第2期8-18,共11页
Command Information System and Technology
基金
国家自然科学基金(61771177和1701454)资助项目
关键词
多传感器信息
探测云
数据一致性
分布式信息融合
multi-sensor information
detection cloud
data consensus
distributed information fusion
