基于改进D-S证据理论的数据融合目标分类

Data Fusion Target Classification Based on Improved D-S Evidence Theory

摘要:

首先采用分类算法对MSTAR数据集进行十类目标分类识别、三类目标的变体分类识别,然后根据分类调参过程中的先验知识修正证据即分类器输出,构造基本置信函数,并采用改进的合成规则即基于冲突系数K和Pignistic概率距离相结合的冲突度量方法,对冲突证据采用按比例分配冲突度的合成规则进行合成。未融合时,三类目标的变体分类准确率最高为93.553%,融合后三类目标变体分类识别率为95.092%,提升幅度约是理想状态的37%。

Abstract:

In this paper, the classification algorithm is used to classify and recognize ten types of targets and three types of target variants on the MSTAR data set. Then, according to the prior knowledge in the classification adjustment process, the evidence is corrected, namely the output of the classifier, and the basic confidence function is constructed. The improved combination rule is a conflict measurement method based on the combination of the conflict coefficient Kand the Pignistic probability distance, and the conflict evidence is synthesized by the combination rule that distributes the conflict degree in proportion. Without fusion, the highest classification accuracy rate of the three types of target variants is 93.553%. After fusion, the classification and recognition rate of the three types of target variants is 95.092%, which is increased by about 37% of that of the ideal state.