D-S证据理论和粗集理论在数据融合中的应用

提出了一种由粗集理论和D S证据理论结合的多传感器数据融合方法 ,并将其应用于目标识别中。在目标识别的数据融合中 ,利用粗集理论对大量的传感器数据进行处理 ,判断出冗余传感器 ,得到传感器的最简组合 ,从而简化特征数据。然后利用D S理论实现目标的分类 ,改进分类的效果。因此 ,将两种方法结合起来应用于数据融合技术中来进行目标识别 ,为解决传感器数据超载以及不完整传感器信息融合提供了一种方法 ,且提高了识别的速度和效果

Application of D-S Evidential Theory and Rough Set Theory in Data Fusion

A multi-sensor data fusion algorithm based on D-S evidence theory and rough set theory is proposed and used in target recognition. In target recognition data fusion, rough set theory is used to process the numerous original data, identity the redundant sensor, obtain the simplest sensor combination,reduce the characteristic data. Then D-S evidence theory is used to perform the target classification so as to improve the effect of classification. These two methods are combined and used in target recognition for solving the problem of sensor data overloading and incomplete sensor information fusion, resulting in improving the speed and effect of recognition.