摘要
通过定义一种新的置信距离,提出了一种改进的一致性数据融合算法.该方法综合考虑各传感器测量精度的差异,并通过权系数来体现各传感器测量精度的差异对置信距离的影响.算例应用表明该方法能够有效地减小传感器发生扰动时数据融合结果的变化,具有较高的数据融合精度;与极大似然法和未改进的一致性数据融合算法比较,该方法融合精度更高、抗干扰能力更强.该改进算法已成功应用于煤自然发火实验温度数据的数据融合,取得了较满意的融合结果.
An improved consensus data fusion algorithm was proposed in this paper by the definition of a new confidence distance. The differences of measured accuracy among different sensors were considered in this algorithm and the influence of measured accuracy among different sensors on confidence distance was reflected through weight factors. The fused results of examples show that the method has higher accuracy and can effectively reduce the variances of data fusion result when the disturbance of sensor occur. Compared with the maximum likelihood method and the existing ones, the improved algorithm can improve the precision, and strengthen the anti-interference capability. This improved algorithm has been applied to temperature data fusion of coal spontaneous combustion experiment successfully and achieved satisfactory results.
出处
《中国矿业大学学报》
EI
CAS
CSCD
北大核心
2009年第4期590-594,共5页
Journal of China University of Mining & Technology
基金
国家自然科学基金项目(50874088)
国家科技支撑计划项目(2007BAK29B03)
新世纪人才支持计划项目(NECT050874)
关键词
多传感器
数据融合
置信距离
煤自然发火
multi-sensor
data fusion
confidence distance
coal spontaneous combustion
