One of the most important open issues is that the classical conflict coefficient in D-S evidence theory (DST) cannot correctly determine the conflict degree between two pieces of evidence. This drawback greatly limits...One of the most important open issues is that the classical conflict coefficient in D-S evidence theory (DST) cannot correctly determine the conflict degree between two pieces of evidence. This drawback greatly limits the use of DST in real application systems. Early researches mainly focused on the improvement of Dempster’s rule of combination (DRC). However, the current research shows it is very important to define new conflict coefficients to determine the conflict degree between two or more pieces of evidence. The evidential sources of information are considered in this work and the definition of a conflict measure function (CMF) is proposed for selecting some useful CMFs in the next fusion work when sources are available at each instant. Firstly, the definition and theorems of CMF are put forward. Secondly, some typical CMFs are extended and then new CMFs are put forward. Finally, experiments illustrate that the CMF based on Jousselme and its similar ones are the best suited ones.展开更多
Belief functions theory is an important tool in the field of information fusion. However, when the cardinality of the frame of discernment becomes large, the high computational cost of evidence combination will become...Belief functions theory is an important tool in the field of information fusion. However, when the cardinality of the frame of discernment becomes large, the high computational cost of evidence combination will become the bottleneck of belief functions theory in real applications. The basic probability assignment (BPA) approximations, which can reduce the complexity of the BPAs, are always used to reduce the computational cost of evidence combination. In this paper, both the cardinalities and the mass assignment values of focal elements are used as the criteria of reduction. The two criteria are jointly used by using rank-level fusion. Some experiments and related analyses are provided to illustrate and justify the proposed new BPA approximation approach.展开更多
基金State Key Development Program for Basic Research of China (2007CB311006)Major Program of National Natural Science Foundation of China (6103200)+8 种基金National Natural Science Foundation of China (60572161, 60874105, 60904099)Excellent Ph.D. Paper Author Foundation of China (200443)Postdoctoral Science Foundation of China (20070421094)Program for New Century Excellent Talents in University (NCET-08-0345)Shanghai Rising-Star Program (09QA-1402900)Aeronautical Science Foundation of China (20090557004)"Chenxing" Scholarship Youth Found of Shanghai Jiaotong University (T241460612)Ministry of Education Key Laboratory of Intelligent Computing & Signal Processing (2009ICIP03)Research Fund of Shaanxi Key Laboratory of Electronic Information System Integration (200910A)
文摘One of the most important open issues is that the classical conflict coefficient in D-S evidence theory (DST) cannot correctly determine the conflict degree between two pieces of evidence. This drawback greatly limits the use of DST in real application systems. Early researches mainly focused on the improvement of Dempster’s rule of combination (DRC). However, the current research shows it is very important to define new conflict coefficients to determine the conflict degree between two or more pieces of evidence. The evidential sources of information are considered in this work and the definition of a conflict measure function (CMF) is proposed for selecting some useful CMFs in the next fusion work when sources are available at each instant. Firstly, the definition and theorems of CMF are put forward. Secondly, some typical CMFs are extended and then new CMFs are put forward. Finally, experiments illustrate that the CMF based on Jousselme and its similar ones are the best suited ones.
基金co-supported by Grant for State Key Program for Basic Research of China(No.2013CB329405)National Natural Science Foundation of China(Nos.61104214,61203222)+3 种基金Foundation for Innovative Research Groups of the National Natural Science Foundation of China(No.61221063)Specialized Research Fund for the Doctoral Program of Higher Education(No.20120201120036)China Postdoctoral Science Foundation(No.20100481337),China Postdoctoral Science Foundation-Special fund(No.201104670)Fundamental Research Funds for the Central Universities
文摘Belief functions theory is an important tool in the field of information fusion. However, when the cardinality of the frame of discernment becomes large, the high computational cost of evidence combination will become the bottleneck of belief functions theory in real applications. The basic probability assignment (BPA) approximations, which can reduce the complexity of the BPAs, are always used to reduce the computational cost of evidence combination. In this paper, both the cardinalities and the mass assignment values of focal elements are used as the criteria of reduction. The two criteria are jointly used by using rank-level fusion. Some experiments and related analyses are provided to illustrate and justify the proposed new BPA approximation approach.