A computationally efficient approximation of Dempster-Shafer theory
International Journal of Man-Machine Studies
On probability-possibility transformations
Fuzzy Sets and Systems
Cooperation under uncertainty in distributed expert systems
Artificial Intelligence
Artificial Intelligence
A first course in fuzzy logic
Uncertainty in Dempster-Shafer theory
Uncertainty in Dempster-Shafer theory
Mathematical Techniques in Multisensor Data Fusion
Mathematical Techniques in Multisensor Data Fusion
Multisensor Data Fusion
Heterogeneous Transformation of Uncertainties of Propositions Among Inexact Reasoning Models
IEEE Transactions on Knowledge and Data Engineering
Constructing the Pignistic Probability Function in a Context of Uncertainty
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
The transferable belief model and other interpretations of Dempster-Shafer's model
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
An uncertainty interchange format with imprecise probabilities
International Journal of Approximate Reasoning
Decision making in the TBM: the necessity of the pignistic transformation
International Journal of Approximate Reasoning
Combining ambiguous evidence with respect to ambiguous a priori knowledge. I. Boolean logic
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Target identification based on the transferable belief model interpretation of dempster-shafer model
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Network-centric warfare (NCW) and the interoperability of joint and coalition forces are among the future warfighting concepts identified by defence. To realise the goals of interoperability and shared situation awareness for NCW, it has long been acknowledged that data fusion is a key enabling technology. Typically, however, distributed data fusion, which is relevant to NCW, and the fusion of disparate types of uncertain data, which is relevant to interoperability, have been investigated separately. Ideally, for shared situation awareness, the system should be capable of performing both aspects of data fusion. In this paper, these facets of data fusion are considered in unison for the automatic target identification problem. In particular, novel Bayesian and generalised Bayesian algorithms are formulated for fusing estimates of target identity generated by local heterogeneous data fusion systems in a network, each of which expresses target identity estimates as either finite probability distributions or Dempster-Shafer belief functions. An example drawn from the literature is used to illustrate the algorithms and their relative performances are assessed in the context of the example to identify issues of possible relevance to distributed target identification in a more general setting. ((C) Commonwealth of Australia 2005.)