一种基于改进的一致性算法数据融合技术(英文)

在重大工程结构健康监测中,随着研究对象复杂程度的提高,往往需要获得大量观测数据才能对结构进行有效的评估,因此采用多种或多个传感器进行测量已成为必然趋势。数据融合技术就是将多个传感器的测量结果进行综合处理,从而得出比单个传感器更为准确可靠的结果。本文基于一致性算法,提出一种改进的多传感器数据融合技术,该数据融合技术属于数据级融合,它克服了一致性算法中两传感器在测量精度不同时置信距离不同的缺点,并对支持矩阵进行模糊化处理,避免了人为定义阈值而产生的主观误差。文中通过算例,验证了此方法可获得较好的结果,并且能够有效地减小由于扰动因素造成的测量数据的变化。

A Data Fusion Method Based on Improved Consensus Algorithm

As structures are becoming more and more complicated,large amounts of measuring data have to be acquired for the effective evaluation of the large-scale civil engineering structures.So it is very necessary to adopt multi-sensors in health monitoring.The purpose of data fusion is to combine and process the data from multi-sensors,thus to obtain much more exact and reliable results than that of the single sensor's.An improved data fusion method for commensurate sensors is presented in this paper.It overcomes the shortcoming of the traditional consensus algorithm with two sensors,which has different confidence distance while measuring in different precision.The relation matrix is fuzzified to avoid the subjective error in determining the threshold value and the effectiveness of this method is confirmed through a numerical example.It can be concluded that this method can get good data fusion result and efficiently reduce the change of the measuring result due to external disturbance.