Iterative estimation of Dempster-Shafer’s basic probability assignment: application to multisensor image segmentation*

2004 ◽  
Vol 43 (6) ◽  
pp. 1293 ◽  
Author(s):  
Abdel-Ouahab Boudraa
Keyword(s):  
Image Segmentation ◽  
Iterative Estimation ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Basic Probability

scholarly journals An improved conflicting-evidence combination method based on the redistribution of the basic probability assignment

2021 ◽  
Author(s):  
Zezheng Yan ◽  
Hanping Zhao ◽  
Xiaowen Mei
Keyword(s):  
Evidence Theory ◽  
Combination Method ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Combination Methods ◽  
Conflicting Evidence ◽  
Basic Probability ◽  
The Right ◽  
Conflict Intensity ◽  
Dempster’S Rule Of Combination

AbstractDempster–Shafer evidence theory is widely applied in various fields related to information fusion. However, the results are counterintuitive when highly conflicting evidence is fused with Dempster’s rule of combination. Many improved combination methods have been developed to address conflicting evidence. Nevertheless, all of these approaches have inherent flaws. To solve the existing counterintuitive problem more effectively and less conservatively, an improved combination method for conflicting evidence based on the redistribution of the basic probability assignment is proposed. First, the conflict intensity and the unreliability of the evidence are calculated based on the consistency degree, conflict degree and similarity coefficient among the evidence. Second, the redistribution equation of the basic probability assignment is constructed based on the unreliability and conflict intensity, which realizes the redistribution of the basic probability assignment. Third, to avoid excessive redistribution of the basic probability assignment, the precision degree of the evidence obtained by information entropy is used as the correction factor to modify the basic probability assignment for the second time. Finally, Dempster’s rule of combination is used to fuse the modified basic probability assignment. Several different types of examples and actual data sets are given to illustrate the effectiveness and potential of the proposed method. Furthermore, the comparative analysis reveals the proposed method to be better at obtaining the right results than other related methods.

scholarly journals Conflict Data Fusion in a Multi-Agent System Premised on the Base Basic Probability Assignment and Evidence Distance

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 820
Author(s):  
Jingyu Liu ◽  
Yongchuan Tang
Keyword(s):  
Data Fusion ◽  
Evidence Theory ◽  
Multi Agent System ◽  
Agent System ◽  
Combination Rules ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Reasonable Evidence ◽  
Basic Probability ◽  
Multi Agent

The multi-agent information fusion (MAIF) system can alleviate the limitations of a single expert system in dealing with complex situations, as it allows multiple agents to cooperate in order to solve problems in complex environments. Dempster–Shafer (D-S) evidence theory has important applications in multi-source data fusion, pattern recognition, and other fields. However, the traditional Dempster combination rules may produce counterintuitive results when dealing with highly conflicting data. A conflict data fusion method in a multi-agent system based on the base basic probability assignment (bBPA) and evidence distance is proposed in this paper. Firstly, the new bBPA and reconstructed BPA are used to construct the initial belief degree of each agent. Then, the information volume of each evidence group is obtained by calculating the evidence distance so as to modify the reliability and obtain more reasonable evidence. Lastly, the final evidence is fused with the Dempster combination rule to obtain the result. Numerical examples show the effectiveness and availability of the proposed method, which improves the accuracy of the identification process of the MAIF system.

Automatic Vehicle Route Prediction based on Multi-Sensor Fusion

2021 ◽  
Vol 13 (4) ◽  
Author(s):  
Huajie Xu ◽  
Baolin Feng ◽  
Yong Peng
Keyword(s):  
Sensor Fusion ◽  
Prediction Algorithm ◽  
Motion Model ◽  
Optimal Motion ◽  
Fusion Algorithm ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Vehicle Route ◽  
Route Prediction ◽  
Basic Probability

To solve the problem of inaccurate results of vehicle routing prediction caused by a large number of uncertain information collected by different sensors in previous automatic vehicle route prediction algorithms, an automatic vehicle route prediction algorithm based on multi-sensor fusion is studied. The process of fusion of multi-sensor information based on the D-S evidence reasoning fusion algorithm is applied to automatic vehicle route prediction. According to the contribution of a longitudinal acceleration sensor and yaw angular velocity sensor detection information to the corresponding motion model, the basic probability assignment function of each vehicle motion model is obtained; the basic probability assignment function of each motion model is synthesized by using D-S evidence reasoning synthesis formula. The new probability allocation of each motion model is obtained under all evidence and then deduced according to the decision rules. Guided by the current optimal motion model, the optimal motion model at each time is used to accurately predict the vehicle movement route. The simulation results show that the prediction error of the algorithm is less than 4% in the process of 30 minutes of automatic vehicle route prediction.

Determination of Basic Probability Assignment Based on Recognition Sequence

2014 ◽  
Vol 1049-1050 ◽  
pp. 1171-1175
Author(s):  
Yan Fei Chen ◽  
Xue Zhi Xia ◽  
Kui Tu
Keyword(s):  
Target Recognition ◽  
Evidence Theory ◽  
Practical Application ◽  
Recognition Sequence ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Recognition Result ◽  
Basic Probability ◽  
Simulation Results

In some practical application of target recognition with sensors, the sensors will give the recognition sequence of targets, which is more detailed than the single recognition result. How to properly construct the basic probability assignment by the recognition sequence becomes the key to successful application of evidence theory. For the recognition sequence of the target recognition results of general sensor is incomplete, and the importance of the types in the recognition sequence is in descending order, this paper proposes a method to construct weights of recognition sequence, the basic probability assignments constructed by the weights are closer to the real recognition results. Simulation results show that this method is more reasonable and effective than the method of contrast.

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