Revisions to the JDL Data Fusion Model

Abstract

The Data Fusion Model maintained by the JDL Data Fusion Group is the most widely-used method for categorizing data fusion-related functions. This paper discusses the current effort to revise and expand this model to facilitate the cost-effective development, acquisition, integration and operation of multi-sensor/multi-source systems.Data fusion involves combining information -in the broadest sense -to estimate or predict the state of some aspect of the universe. These may be represented in terms of attributive and relational states. If the job is to estimate the state of a people (or any other sentient beings), it can be useful to include consideration of informational and perceptual states in addition to the physical state.Developing cost-effective multi-source information systems requires a standard method for specifying data fusion processing and control functions, interfaces, and associated data bases. The lack of common engineering standards for data fusion systems has been a major impediment to integration and re-use of available technology. There is a general lack of standardized -or even well-documented -performance evaluation, system engineering methodologies, architecture paradigms, or multi-spectral models of targets and collection systems. In short, current developments do not lend themselves to objective evaluation, comparison or re-use. This paper reports on proposed revisions and expansions of the JDL Data Fusion model to remedy some of these deficiencies. This involves broadening the functional model and related taxonomy beyond the original military focus, and integrating the Data Fusion Tree Architecture model for system description, design and development. What is Data Fusion? What isn't?Data fusion is an increasingly important element of diverse weapon, intelligence, and commercial systems. Data fusion uses overlapping information to determine relationships Approved for public release; distribution is unlimited.

Survey of context information fusion for ubiquitous Internet-of-Things (IoT) systems

“…Architectures based on context information-based systems are centered on the abstraction of the data generated during context fusion. The JDL model [77] and the Dasarathy model [50] are two variants in this class. The JDL model was conceived jointly by the U.S. Joint Directors of Laboratory (JDL) and U.S. Department of Defense (DOD).…”

Insights and characterization of l<inf>1</inf>-norm based sparsity learning of a lexicographically encoded capacity vector for the Choquet integral

“…In [2], L.Hu et al describe it as a tool that can be used in kernel theory to provide an elegant way to map multi-source heterogeneous data into a single combined homogeneous (implicit) space for pattern recognition (feature level fusion). In [3], Joint…”

Issues and challenges in higher level fusion: Threat/impact assessment and intent modeling (a panel summary)

“…Indeed, the original model [3] used 'Threat Assessment' as the general name for level 3 fusion; indicative of the importance of that topic. In subsequent revisions [4], the concept of level 3 has been broadened to that of Impact Assessment. Steinberg goes on to decompose threat into capability, opportunity, and intent as principal factors in predicting (intentional) actions.…”

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