基于信任度的多传感器数据融合及其应用

Multi-sensor data fusion method based on belief degree and its applications

针对多传感器信息采集系统中的数据不确定性问题,提出了一种基于信任度的多传感器数据融合方法.该方法首先定义一个模糊型指数信任度函数,对两传感器测得数据间的信任程度进行量化处理,并通过信任度矩阵度量各传感器测得数据的综合信任程度,以合理地分配测得数据在融合过程中所占权重,得到数据融合估计的最终表达式,从而实现了多传感器数据的融合.分析土壤含水率的数据融合结果可知,应用所提出方法使融合结果的标准差降低至0.0084,随机干扰下变化幅度仅为0.0027%,不仅达到了比传统方法更高的融合精度,而且具有良好的抗干扰性.

Focusing on the uncertainty of data in information gathering system, a multi-sensor data fusion method based on belief degree is proposed, in which a fuzzy-index belief degree function is defined to quantify the belief degree between data measured by two sensors, and the synthesis belief degree of data from various sensors is measured through a belief degree matrix. The weights of data measured in the fusion process are reasonably assigned, so that the final expression of data fusion and estimation is obtained, thus the data fusion of multi-sensor is realized. Through applying the proposed method to the real-world dataset of data fusion in soil water content, it is demonstrated that this method, which can make the standard deviation of fusion results drop to 0.0084 and the varying scope with random disturbance to 0.0027%, not only can reach higher fusion precision than traditional methods, but also has excellent restraint ability against disturbance.