基于多分类支持向量机和证据合成方法的多传感器信息融合研究  

Research of Multi-Sensor Information Fusion Based on Multi-Class Classification SVM and Improved Evidence Combination Method

作　　者：苏思[1] 姜礼平[1] 邹明[1] 

机构地区：[1]海军工程大学理学院,湖北武汉430033

出　　处：《兵工自动化》2010年第1期59-62,共4页Ordnance Industry Automation

基　　金：海军工程大学科研基金资助课题(hjsk200811)

摘　　要：为克服支持向量机和Dempster方法的不足,提出一种基于SVM多分类器的识别结果概率输出方法。以BPA函数的形式输出SVM多分类结果,对Dempster证据合成方法进行了改进:根据证据之间的相似性程度判断是否存在证据冲突,对于证据数目在3条以上且存在冲突的证据组合,引用统计理论中马氏距离的计算公式计算各证据与其余证据组合之间的距离,导出各证据的重要性权系数,由此对证据的BPA函数进行转化,采用Dempster方法对转化后的BPA函数进行合成。然后,将SVM多分类器的BPA输出作为参与融合的各传感器对应的BPA函数,采用改进的证据合成方法进行合成,得到最后识别结果。结果表明,SVM识别方法能降低错误率,其输出形式包含更多信息量。To overcome the shortage of SVM and Dempster＇s method, a new method of probabilistic outputs based on multi-class classification SVM is presented. The classification results are used as BPA functions, an improved evidence combination method is presented： the similarities between evidences are used as an approach judging whether conflicts exist, if there are more than 3 evidences as well as conflicts, the Mahalanobis Distance algorithm is used to calculate the distance between each evidence and the other groups of evidences so as to obtain the evidences＇ weight coefficients. By means of these coefficients, BPA functions are transformed, and the Dempster＇s method is used for the combination. Then, the probabilistic outputs of multi-class classification SVM are taken as BPA functions, the improved evidence combination method is used to fulfill the combination. Simulation results show that the output＇s error rate is reduced, as well as the quantity of information is increased.

关 键 词：支持向量机 证据理论 信息融合 多传感器 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]