一种快速分层递阶DSmT近似推理融合方法(A)

本文提出了一种分层递阶的DSmT快速近似推理融合方法,该方法针对超幂集空间中仅单子焦元具有信度赋值的情况,利用二叉树或三叉树分组技术对其刚性分组,与此同时,对每个信息源对应的各个分组焦元进行信度赋值求和,以便实现细粒度超幂集空间向粗粒度超幂集空间映射.然后运用DSmT组合规则和比例冲突分配规则对粗化超幂集空间的两个信息源进行融合,保存该融合结果作为父子之间节点连接权值,然后对每个分组焦元信度赋值归一化处理,通过设定树的深度,来确定分层递阶的次数.最后通过从多个角度比较新、老方法,从而充分地验证了新方法的优越性.

一种快速分层递阶DSmT近似推理融合方法(B)

针对Dezert-Smarandache Theory(DSmT),随着鉴别框架中焦元数目的增多,其组合推理运算成指数增长,已成为制约该理论广泛应用与发展的瓶颈问题.为了解决这个难题,本文在进一步深入研究仅单子焦元赋值几个关键问题的基础上,主要针对超幂集空间中部分单子和冲突焦元具有信度赋值的情况,通过比例分配原则,把冲突焦元的信度赋值分配到相应的单子焦元上,然后根据仅单子焦元情形下的近似推理方法进行处理,即利用二叉树分组技术对单子焦元进行刚性分组,实现细粒度超幂集空间向粗粒度超幂集空间映射.最后通过从计算效率、信息损失和相似度的角度分别比较新、老方法,比较结果充分地验证了新方法的优越性.

基于ESMS过滤器的信息融合理论研究及SLAM应用

本文解决了多源信息融合时信息源选择的难题,提出了一种广义的证据支持贴近度过滤器来选择最一致的证据源,并耦合基于DSmT和PCR5的融合机,应用于PioneerII移动机器人的SLAM;通过对运行在虚拟环境中的一个虚拟机器人(自身携带16个Sonar传感器),感知周围环境信息,对有或没有ESMS过滤器两种情况下的环境地图重构效果进行比较,充分验证了ESMS过滤器作为信息融合源选择先决条件的优点。

Multi-source information fusion:Progress and future

Multi-Source Information Fusion(MSIF),as a comprehensive interdisciplinary field based on modern information technology,has gained significant research value and extensive application prospects in various domains,attracting high attention and interest from scholars,engineering experts,and practitioners worldwide.Despite achieving fruitful results in both theoretical and applied aspects over the past five decades,there remains a lack of comprehensive and systematic review articles that provide an overview of recent development in MSIF.In light of this,this paper aims to assist researchers and individuals interested in gaining a quick understanding of the relevant theoretical techniques and development trends in MSIF,which conducts a statistical analysis of academic reports and related application achievements in the field of MSIF over the past two decades,and provides a brief overview of the relevant theories,methodologies,and application domains,as well as key issues and challenges currently faced.Finally,an analysis and outlook on the future development directions of MSIF are presented.

Novel moderate transformation of fuzzy membership function into basic belief assignment

In information fusion,the uncertain information from different sources might be modeled with different theoretical frameworks.When one needs to fuse the uncertain information represented by different uncertainty theories,constructing the transformation between different frameworks is crucial.Various transformations of a Fuzzy Membership Function(FMF)into a Basic Belief Assignment(BBA)have been proposed,where the transformations based on uncertainty maximization and minimization can determine the BBA without preselecting the focal elements.However,these two transformations that based on uncertainty optimization emphasize the extreme cases of uncertainty.To avoid extreme attitudinal bias,a trade-off or moderate BBA with the uncertainty degree between the minimal and maximal ones is more preferred.In this paper,two moderate transformations of an FMF into a trade-off BBA are proposed.One is the weighted average based transformation and the other is the optimization-based transformation with weighting mechanism,where the weighting factor can be user-specified or determined with some prior information.The rationality and effectiveness of our transformations are verified through numerical examples and classification examples.

一种快速分层递阶DSmT近似推理融合方法(C)

针对超幂集空间中部分不确定焦元或者混合焦元具有信度赋值的情形,将混合焦元转化为统一形式;根据纯不确定焦元平分原则进行平分信度赋值,并将新的或固有的冲突焦元的信度赋值分配到相应的单子焦元上;利用二叉树分组技术对单子焦元进行刚性分组,实现细粒度超向粗粒度超幂集空间的映射;运用DSmT组合规则和比例冲突分配规则对粗化超幂集空间的多个信息源进行融合,保存该融合结果作为父子之间节点连接权值;对每个分组焦元信度赋值归一化处理,通过设定树的深度,来确定分层递阶的次数;最后从计算效率、信息损失和相似度的角度分别就新、老方法进行比较,结果验证了新方法的优越性.

A new non-specificity measure in evidence theory based on belief intervals

In the theory of belief functions, the measure of uncertainty is an important concept, which is used for representing some types of uncertainty incorporated in bodies of evidence such as the discord and the non-specificity. For the non-specificity part, some traditional measures use for reference the Hartley measure in classical set theory; other traditional measures use the simple and heuristic function for joint use of mass assignments and the cardinality of focal elements. In this paper, a new non-specificity measure is proposed using lengths of belief intervals, which represent the degree of imprecision. Therefore, it has more intuitive physical meaning. It can be proved that our new measure can be rewritten in a general form for the non-specificity. Our new measure is also proved to be a strict non-specificity measure with some desired properties. Numerical examples, simulations, the related analyses and proofs are provided to show the characteristics and good properties of the new non-specificity definition. An example of an application of the new non- specificity measure is also presented.

Combination of Qualitative Information with 2-Tuple Linguistic Representation in DSmT

Modern systems for information retrieval, fusion and management need to deal more and more with information coming from human experts usually expressed qualitatively in natural language with linguistic labels. In this paper, we propose and use two new 2-Tuple linguistic representation models （i.e., a distribution function model （DFM） and an improved Herrera-Martinez＇s model） jointly with the fusion rules developed in Dezert-Smarandache Theory （DSmT）, in order to combine efficiently qualitative information expressed in term of qualitative belief functions. The two models both preserve the precision and improve the efficiency of the fusion of linguistic information expressing the global expert＇s opinion. However, DFM is more general and efficient than the latter, especially for unbalanced linguistic labels. Some simple examples are also provided to show how the 2-Tuple qualitative fusion rules are performed and their advantages.

Basic belief assignment approximations using degree of non-redundancy for focal element

Dempster-Shafer evidence theory, also called the theory of belief function, is widely used for uncertainty modeling and reasoning. However, when the size and number of focal elements are large, the evidence combination will bring a high computational complexity. To address this issue,various methods have been proposed including the implementation of more efficient combination rules and the simplifications or approximations of Basic Belief Assignments(BBAs). In this paper,a novel principle for approximating a BBA into a simpler one is proposed, which is based on the degree of non-redundancy for focal elements. More non-redundant focal elements are kept in the approximation while more redundant focal elements in the original BBA are removed first. Three types of degree of non-redundancy are defined based on three different definitions of focal element distance, respectively. Two different implementations of this principle for BBA approximations are proposed including a batch and an iterative type. Examples, experiments, comparisons and related analyses are provided to validate proposed approximation approaches.

De-combination of belief function based on optimization

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.

Combination of classifiers with incomplete frames of discernment

The methods for combining multiple classifiers based on belief functions require to work with a common and complete(closed)Frame of Discernment(Fo D)on which the belief functions are defined before making their combination.This theoretical requirement is however difficult to satisfy in practice because some abnormal(or unknown)objects that do not belong to any predefined class of the Fo D can appear in real classification applications.The classifiers learnt using different attributes information can provide complementary knowledge which is very useful for making the classification but they are usually based on different Fo Ds.In order to clearly identify the specific class of the abnormal objects,we propose a new method for combination of classifiers working with incomplete frames of discernment,named CCIF for short.This is a progressive detection method that select and add the detected abnormal objects to the training data set.Because one pattern can be considered as an abnormal object by one classifier and be committed to a specific class by another one,a weighted evidence combination method is proposed to fuse the classification results of multiple classifiers.This new method offers the advantage to make a refined classification of abnormal objects,and to improve the classification accuracy thanks to the complementarity of the classifiers.Some experimental results are given to validate the effectiveness of the proposed method using real data sets.