Uncertainty and vagueness in knowledge based systems
Uncertainty and vagueness in knowledge based systems
Approximations for efficient computation in the theory of evidence
Artificial Intelligence
Artificial Intelligence
Uncertainty-Based Information: Elements of Generalized Information Theory
Uncertainty-Based Information: Elements of Generalized Information Theory
Decision making in the TBM: the necessity of the pignistic transformation
International Journal of Approximate Reasoning
Sensor fusion in anti-personnel mine detection using a two-level belief function model
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Editorial: Special issue in memory of Philippe Smets (1938--2005)
International Journal of Approximate Reasoning
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
In Memoriam: Philippe Smets (1938--2005)
Fuzzy Sets and Systems
In memoriam: Philippe Smets (1938-2005)
International Journal of Approximate Reasoning
The geometry of consonant belief functions: Simplicial complexes of necessity measures
Fuzzy Sets and Systems
Evidence supporting measure of similarity for reducing the complexity in information fusion
Information Sciences: an International Journal
International Journal of Approximate Reasoning
Distances in evidence theory: Comprehensive survey and generalizations
International Journal of Approximate Reasoning
A distance between continuous belief functions
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
Generic discounting evaluation approach for urban image classification
IUKM'13 Proceedings of the 2013 international conference on Integrated Uncertainty in Knowledge Modelling and Decision Making
Information-based dissimilarity assessment in Dempster-Shafer theory
Knowledge-Based Systems
How to preserve the conflict as an alarm in the combination of belief functions?
Decision Support Systems
Hi-index | 0.00 |
The problem is sequential association of combat ID declarations in the multi-target environment. Being random and non-specific, the combat ID declarations are represented by belief functions and manipulated using the tools of the belief function theory as interpreted by the transferable belief model (TBM). The solution is provided in the framework of ''object to ID declaration'' association based on assignment techniques. For that purpose, the paper derives the global cost of assignment (i.e. a dissimilarity measure) based on the plausibility of the global assignment. This measure is directly related to the conflict as described in the TBM. The performance of the proposed method was evaluated by Monte Carlo simulations and a comparison with various alternative dissimilarity measures is carried out in the framework of multi-object classification.