Journal ArticleDOI
A target identification comparison of Bayesian and Dempster-Shafer multisensor fusion
Dennis M. Buede,P. Girardi +1 more
- Vol. 27, Iss: 5, pp 569-577
TLDR
This paper demonstrates how Bayesian and evidential reasoning can address the same target identification problem involving multiple levels of abstraction, such as identification based on type, class, and nature, and shows that probability theory can accommodate all of these issues that are present in dealing with uncertainty.Abstract:
This paper demonstrates how Bayesian and evidential reasoning can address the same target identification problem involving multiple levels of abstraction, such as identification based on type, class, and nature. In the process of demonstrating target identification with these two reasoning methods, we compare their convergence time to a long run asymptote for a broad range of aircraft identification scenarios that include missing reports and misassociated reports. Our results show that probability theory can accommodate all of these issues that are present in dealing with uncertainty and that the probabilistic results converge to a solution much faster than those of evidence theory.read more
Citations
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Journal ArticleDOI
Data fusion
Jens Bleiholder,Felix Naumann +1 more
TL;DR: This article places data fusion into the greater context of data integration, precisely defines the goals of data fusion, namely, complete, concise, and consistent data, and highlights the challenges of data Fusion.
Recursive Bayesian Estimation : Navigation and Tracking Applications
TL;DR: This thesis phrases the application of terrain navigation in the Bayesian framework, and develops a numerical approximation to the optimal but intractable recursive solution, and derives explicit expressions for the Cramer-Rao bound of general nonlinear filtering, smoothing and prediction problems.
Journal ArticleDOI
The Dempster-Shafer theory of evidence: an alternative approach to multicriteria decision modelling
TL;DR: The objective of this paper is to describe the potential offered by the Dempster–Shafer theory of evidence as a promising improvement on “traditional” approaches to decision analysis.
Journal ArticleDOI
Assessing sensor reliability for multisensor data fusion within the transferable belief model
TL;DR: A method for assessing the reliability of a sensor in a classification problem based on the transferable belief model based on finding the discounting factor minimizing the distance between the pignistic probabilities computed from the discounted beliefs and the actual values of data.
Journal ArticleDOI
Probability, possibility and evidence: approaches to consider risk and uncertainty in forestry decision analysis
Annika Kangas,Jyrki Kangas +1 more
TL;DR: The aim is to provide readers with an overview of alternative approach for coping with uncertainty, especially from the viewpoint of forestry and natural resource management applications, as well as some decision support methods designed using the approaches presented.
References
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Book
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
Book
A mathematical theory of evidence
TL;DR: This book develops an alternative to the additive set functions and the rule of conditioning of the Bayesian theory: set functions that need only be what Choquet called "monotone of order of infinity." and Dempster's rule for combining such set functions.
Book ChapterDOI
A generalization of bayesian inference
TL;DR: Procedures of statistical inference are described which generalize Bayesian inference in specific ways Probability is used in such a way that in general only bounds may be placed on the probabilities of given events, and probability systems of this kind are suggested both for sample information and for prior information as discussed by the authors.
Book
Advances in the Dempster-Shafer theory of evidence
TL;DR: The Dempster-Shafer Theory of Evidence is applied as a guide for the management of uncertainty in knowledge-based systems.
Journal ArticleDOI
An evidential reasoning approach for multiple-attribute decision making with uncertainty
Jian-Bo Yang,Madan G. Singh +1 more
TL;DR: A decision making procedure is proposed to rank alternatives in MADM problems with uncertainty to deal with uncertain decision knowledge in multiple-attribute decision making (MADM) problems with both quantitative and qualitative attributes.