| De-combination of belief function based on optimization |
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| 作者姓名： | Xiaojing FAN Deqiang HAN Yi YANG Jean DEZERT |
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| 作者单位： | 1. School of Automation Science and Engineering, Xi'an Jiaotong University;2. SKLSVMS, School of Aerospace, Xi'an Jiaotong University |
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| 基金项目： | supported by the National Natural Science Foundation of China (No. 61671370)；;the Postdoctoral Science Foundation of China (No. 2016M592790)；;the Postdoctoral Science Research Foundation of Shaanxi Province, China (No. 2016BSHEDZZ46)； |
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| 摘 要： | In the theory of belief functions, the evidence combination is a kind of decision-level information fusion. Given two or more Basic Belief Assignments(BBAs) originated from different information sources, the combination rule is used to combine them to expect a better decision result. When only a combined BBA is given and original BBAs are discarded, if one wants to analyze the difference between the information sources, evidence de-combination is needed to determine the original BBAs. Evidence d...
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| 收稿时间： | 2 March 2021 |
De-combination of belief function based on optimization |
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| Xiaojing FAN,Deqiang HAN,Yi YANG,Jean DEZERT.De-combination of belief function based on optimization[J].Chinese Journal of Aeronautics,2022,35(5):179-193. |
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| Institution: | 1. School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China;2. SKLSVMS, School of Aerospace, Xi’an Jiaotong University, Xi’an 710049, China;3. ONERA, The French Aerospace Lab, Palaiseau 91761, France |
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| Abstract: | In the theory of belief functions, the evidence combination is a kind of decision-level information fusion. Given two or more Basic Belief Assignments (BBAs) originated from different information sources, the combination rule is used to combine them to expect a better decision result. When only a combined BBA is given and original BBAs are discarded, if one wants to analyze the difference between the information sources, evidence de-combination is needed to determine the original BBAs. Evidence de-combination can be considered as the inverse process of the information fusion. This paper focuses on such a defusion of information in the theory of belief functions. It is an under-determined problem if only the combined BBA is available. In this paper, two optimization-based approaches are proposed to de-combine a given BBA according to the criteria of divergence maximization and information maximization, respectively. The new proposed approaches can be used for two or more information sources. Some numerical examples and an example of application are provided to illustrate and validate our approaches. |
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| Keywords: | Belief functions De-combination Divergence maximization Information fusion Information maximization |
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