基于自适应分批估计的瓦斯监测多传感器数据融合研究  

Study of multi-sensor data fusion based on adaptive batch estimation algorithm for gas monitoring

作　　者：孙克雷[1] 秦汝祥[1] 

机构地区：[1]安徽理工大学计算机科学与工程学院,安徽淮南232001

出　　处：《传感器与微系统》2011年第10期47-49,共3页Transducer and Microsystem Technologies

基　　金：国家自然科学基金资助项目(50974003)

摘　　要：针对瓦斯监测中多传感器监测数据的融合问题,提出了一种多源数据自适应分批估计算法。利用各组传感器局部融合值与最终融合值的方差自适应地调节各组的权重,通过多步融合逐渐弱化误差较大传感器组对最终融合值的影响。仿真实验表明:相对于平均值法与分批估计算法,该算法能有效地提高数据融合精度,能够满足瓦斯监测对实时性和精确性的要求。Aimed at multi-sensor data fusion for gas monitoring, a multi-source data fusion algorithm based on adaptive theory and batch estimation algorithm is conducted. The variance of each group partial fusion value and the final fusion value is used to adjust the weight of each group adaptively. This algorithm can weaken the influence of the group with big error through multi-step fusion. The simulation experiment shows that this method can improve the precision of data fusion compared with the average value method and batch estimation algorithm. It can meet real-time and accuracy requirements for gas monitoring.

关 键 词：自适应分批估计 数据融合 瓦斯监测 多传感器 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]