Dempster-Shafer reasoning with application multisensor object recognition system

Firstly by analyzing comprehensively the different sensor acquisitions based on normal distribution, a mathematical model is established to attain the basic probability assignments. Following this a multisensor data fusion method-based Dempster-Shafer reasoning is proposed to resolve object recognition problems. Offered by multilevel accumulation of assignments recursively the fusion estimates based on global information is obtained to testify the recognition performance. It is optimal than that of a single sensor with an average decline 74 percent of uncertainty value in a case study of pyrites recognition system, thereby demonstrating the effectiveness and correctness of this approach.