一种在线迭代聚类的多传感器一致性数据融合方法

An Online Iterative Clustering Method for Multi Sensor Consistent Data Fusion

摘要:

为了提高多传感器一致性数据融合的效果，在进行多传感器测量数据融之前，必须要对各传感器的测量数据是否具有一致性进行判定处理。针对现有各种多传感器一致性数据融合方法存在的不足，文章运用统计学中的假设检验理论、聚类分析和正态分布的优良性质，提出了一种在线迭代聚类的多传感器一致性数据融合方法，用Matlab进行大量仿真实验的结果表明，本文给出方法优于现有的多传感器一致性数据融合方法。

Abstract:

In order to improve the effect of multi-sensor consistent data fusion, before multi-sensor measurement data fusion, it is necessary to determine whether the measurement data of each sensor is consistent or not. In view of the shortcomings of the existing multi-sensor consistency data fusion methods, this paper proposes an online iterative clustering multi-sensor consistency data fusion method based on the hypothesis test theory, clustering analysis and normal distribution. A large number of simulation experiments with MATLAB show that the proposed method is better than the existing multi-sensor consistent data fusion method.