摘要
在多传感器信息融合中,DS证据理论是一种重要方法,但是它的基石基本概率分配(BPA)一般不易确定,从而使它的优势难以得到发挥。支持向量机(SVM)是建立在统计学习理论之上的一种新型学习算法,但SVM的硬判决输出却不便于进行多传感器信息融合。为便于信息融合,本文提出了一种具有BPA输出的二类SVM,通过分析Platt概率输出模型的实质与不足提出利用SVM精度下限对其进行加权处理来得到证据理论的BPA方法,实现了SVM与DS证据理论在信息融合中的结合。仿真结果表明通过本文方法可以实现多传感器的信息融合并大大降低了融合识别的误差率。
DS evidence theory is an important method in the field of multi-sensor information fusion, but its advantage is not fully utilized because its BPA is difficult to obtain. SVM is a new learning algorithm based on the statistical learning theory. However, its hard decision output does not adequately facilitate multisensor information fusion. In this paper, in order to apply SVM to information fusion, a two-class SVM with BPA output is proposed. By analyzing the essence and deficiency of the Platt＇s model, the BPA is obtained through use of the lower bound of the SVM precision to weight the Platt＇s probability model, which achieves the combination of SVM and the evidence theory in the information fusion. The simulation results show that multi-sensor information fusion can be realized and the error rate can be greatly lowered through the algorithm proposed in this paper.
出处
《传感技术学报》
CAS
CSCD
北大核心
2008年第9期1566-1570,共5页
Chinese Journal of Sensors and Actuators
关键词
信息融合
支持向量机
证据理论
基本概率分配
information fusion, support vector machine, evidence theory, basic probability assignment
