scholarly journals Multisensor Data Fusion Based on Modified D-S Evidence Theory

2018 ◽  
Author(s):  
Yingming Zhou ◽  
Hongji Xu ◽  
Junfeng Sun ◽  
Lingling Pan ◽  
Baozhen Du ◽  
...  
Keyword(s):  
Data Fusion ◽  
Evidence Theory ◽  
Multisensor Data Fusion ◽  
Multisensor Data

scholarly journals Multisensor Data Fusion in Testability Evaluation of Equipment

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Peng Di ◽  
Xuan Wang ◽  
Tong Chen ◽  
Bin Hu
Keyword(s):  
Data Fusion ◽  
Test Data ◽  
Evaluation Method ◽  
Evidence Theory ◽  
Uncertain Information ◽  
Fusion Method ◽  
Practical Applications ◽  
Multisensor Data Fusion ◽  
Basic Probability ◽  
Multisensor Data

The multisensor data fusion method has been extensively utilized in many practical applications involving testability evaluation. Due to the flexibility and effectiveness of Dempster–Shafer evidence theory in modeling and processing uncertain information, this theory has been widely used in various fields of multisensor data fusion method. However, it may lead to wrong results when fusing conflicting multisensor data. In order to deal with this problem, a testability evaluation method of equipment based on multisensor data fusion method is proposed. First, a novel multisensor data fusion method, based on the improvement of Dempster–Shafer evidence theory via the Lance distance and the belief entropy, is proposed. Next, based on the analysis of testability multisensor data, such as testability virtual test data, testability test data of replaceable unit, and testability growth test data, the corresponding prior distribution conversion schemes of testability multisensor data are formulated according to their different characteristics. Finally, the testability evaluation method of equipment based on the multisensor data fusion method is proposed. The result of experiment illustrated that the proposed method is feasible and effective in handling the conflicting evidence; besides, the accuracy of fusion of the proposed method is higher and the result of evaluation is more reliable than other testability evaluation methods, which shows that the basic probability assignment of the true target is 94.71%.

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