基于测量值模糊贴近度时空融合的多传感器融合方法  

Multi-sensor Data Fusion Method Based on Measurement Fuzzy Clingy Degree Spatial-Temporal

作　　者：黄宗建[1] 母军臣[2] 罗志斌[2] 刘骥宇[2] 

机构地区：[1]河南工业职业技术学院电气工程系,南阳473009 [2]河南大学计算机与信息工程学院,开封475001

出　　处：《宇航计测技术》2007年第2期25-28,共4页Journal of Astronautic Metrology and Measurement

摘　　要：针对实际多传感器目标跟踪系统中,传感器测量值中的野值对状态估计的不利影响,该算法利用概率源合并理论和非负矩阵特征向量理论,算出k时刻空间上各传感器测量值与其他传感器测量值的综合贴近度,同时将k个时刻的空间上融合的模糊贴近度进行时间融合,以此确定每个传感器的权重。仿真结果表明,该算法有效地抑制了测量野值对测量融合值的不利影响,提高了系统的跟踪准确度。In actual target tracking system of the sensors data fusion, for the bad influences of the measurement values including outliers to state estimate, the algorithm calculates the fuzzy clingy degree of measurement value of arbitrary two sensors on space based on the theories of probability source combination and characteristic vector of non-negative matrix, at the same time the synthetic fuzzy clingy degree of each sensor measurement value and other sensors measurement values is fused in time, and the weight of each sensor. The simulation results show that the modified algorithms can restrain the bad influences of outliers and improve the tracking accuracy.

关 键 词：测量 野值 模糊贴近度 时空融合 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]