Sammenlign metoder

	Data Fusion	Dempster-Shafer-teorien om evidens
Fagområde	Datafusion	Soft computing
Familie	Process / pipeline	Machine learning
Oprindelsesår	1997	1976
Ophavsperson	David Hall & James Llinas	Arthur P. Dempster & Glenn Shafer
Type	Multi-level information integration pipeline	Uncertainty calculus for combining evidence
Oprindelig kilde	Hall, D. L., & Llinas, J. (1997). An introduction to multisensor data fusion. Proceedings of the IEEE, 85(1), 6–23.	Dempster, A. P. (1967). Upper and lower probabilities induced by a multivalued mapping. The Annals of Mathematical Statistics, 38(2), 325–339.
Aliasser	Sensor Data Fusion, Information Fusion, Multi-source Data Fusion, Veri Füzyonu	evidence theory, belief functions, evidential reasoning, Dempster-Shafer kanıt teorisi
Relaterede	3	4
Resumé	Data fusion is a multi-level process that combines data and information from multiple sensors and sources to achieve improved accuracy, completeness, and confidence in estimates that cannot be obtained from any single source alone. Formally introduced as the Joint Directors of Laboratories (JDL) model by Hall and Llinas in 1997, the framework organizes fusion into hierarchical processing levels ranging from raw signal combination to higher-order situation and threat assessment.	Dempster-Shafer theory is a mathematical framework for reasoning under uncertainty that generalizes Bayesian probability by representing ignorance explicitly. Instead of forcing a single probability on each hypothesis, it assigns belief mass to sets of hypotheses and derives a belief-plausibility interval, and it provides Dempster's rule for fusing evidence from multiple independent sources. Developed from Arthur Dempster's 1967 work and Glenn Shafer's 1976 monograph, it underpins evidential reasoning and sensor/decision fusion.