Dempster-Shafer evidence theory is an efficient method to process uncertain, incomplete and vague information in data fusion. The combination of conflicting bodies of evidence has been one of the most important issues in Dempster-Shafer evidence theory since the example of counter-intuitive results produced by Dempster’s rule was found. Based on the framework of the Dempster-Shafer evidence the...

This paper develops a new uncertainty measure for the theory of hints that complies with the established semantics of statistical information theory and further satisfies all classical requirements for such a measure imposed in the literature. The proposed functional decomposes into conversant uncertainty measures and therefore discloses a new interpretation of the latters as well. By abstracti...

The Dempster–Shafer theory (DST) may be considered as a generalization of the probability theory, which assigns mass values to the subsets of the referential set and suggests an interval-valued probability measure. There have been several attempts for fuzzy generalization of the DST by assigning mass (probability) values to the fuzzy subsets of the referential set. The interval-valued probabili...

This paper describes how soft computing methodologies such as fuzzy logic, genetic algorithms and the Dempster–Shafer theory of evidence can be applied in a mobile robot navigation system. The navigation system that is considered has three navigation subsystems. The lower-level subsystem deals with the control of linear and angular volocities using a multivariable PI controller described with a...

A very computationally-efficient MonteCarlo algorithm for the calculation of Dempster-Shafer belief is described. If Bel is the combination using Dempster’s Rule of belief functions Bel1, . . . ,Belm then, for subset b of the frame Θ, Bel(b) can be calculated in time linear in |Θ| and m (given that the weight of conflict is bounded). The algorithm can also be used to improve the complexity of t...

In this paper we develop methods for selection of templates and use these templates to recluster an already performed Dempster-Shafer clustering taking into account intelligence to template fit during the reclustering phase. By this process the risk of erroneous force aggregation based on some misplace pieces of evidence from the first clustering process is greatly reduced. Finally, a more reli...

The process of information fusion needs to deal with a large number uncertain multi-source, heterogeneity, inaccuracy, unreliability, and incompleteness. In practical engineering applications, Dempster–Shafer evidence theory is widely used in multi-source owing its effectiveness data fusion. Information sources have an important impact on environment the characteristics complex, unstable, uncer...