Revisiting the JDL Data Fusion Model II

By J. Llinas, C. Bowman, G. Rogova, A. Steinberg, E. Waltz, F. White. 2004

456 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b183008f9d63252bd4cc4438cf0bf744bc8e995d

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Citations in the corpus

Towards unbiased evaluation of uncertainty reasoning: The URREF ontology (2012, background): “The Joint Director of the Lab’s (JDL) model and its subsequent revisions formed the high-low level distinction [1-5].”
Integrating hard and soft information sources for D2D using controlled natural language (2012, background): “1 D2D emphasises the collection and fusion of actionable information, to provide a clear picture of options, threats, and consequences [1], [2].”
Fusion Techniques for Reliable Information: A Survey (2010, methodology): “The extensions of JDL model refine any generalized data fusion model description by adapting important issues [57].”
Information Fusion for Simulation Based Decision Support in manufacturing (2006, methodology): “One model for IF is the JDL model [9, 10].”
A Review of Data Fusion Techniques (2013, methodology): “Llinas et al. [6] propose several refinements and extensions to the JDL model.”
Multisensor data fusion: A review of the state-of-the-art (2013, background): “Despite its popularity, the JDL model has many shortcomings, such as being too restrictive and especially tuned to military applications, which have been the subject of several extension proposals [13,14] attempting to alleviate them.”
Opinion Mining and Information Fusion: A survey (2016, background): “The JDL Fusion Model was originally designed for addressing the combined effects of different levels of abstraction and problem-space complexity, and was divided in 5 levels at which fusion could be performed [137, 138].”