一种基于灰色定权聚类的决策层融合目标识别算法  

A Method of Decision Fusion for Target Recognition Based on Grey Fixed Weight Clustering

作　　者：贾宇平[1] 李亚楠[1] 付耀文[1] 庄钊文[1] 

机构地区：[1]国防科技大学电子科学与工程学院,长沙410073

出　　处：《电子与信息学报》2008年第2期255-258,共4页Journal of Electronics & Information Technology

摘　　要：决策层融合目标识别本质上是一个不确定性信息处理问题,该文基于灰色系统理论中的灰色定权聚类方法提出了一种新的决策层融合目标识别算法。该算法将一个M类目标识别问题转化为M个两类目标识别问题,然后对每个两类识别问题采用灰色定权聚类的方法解决,其中白化权函数通过训练样本学习确定。利用该算法对3种分类器识别5类雷达目标的结果进行融合,实验结果表明该方法能有效提高目标识别性能。The problem of decision fusion for target recognition is usually solved by the uncertainty information processing methods. This paper presents a novel algorithm of decision fusion for target recognition based on the grey fixed weight clustering analysis. The problem of recognizing M classes target is transformed into M problems of recognizing two classes target, and then each problem of recognizing two classes target is solved by the grey fixed weight clustering. The whitenization weight functions of each two classes are assessed by training samples. The experiment conducted on three classifiers and five classes＇ radar target data demonstrates this method can effectively improve the recognition performance.

关 键 词：目标识别 灰色定权聚类 白化权函数 信息融合 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]