Share Email Print
cover

Proceedings Paper

Revisions to the JDL data fusion model
Author(s): Alan N. Steinberg; Christopher L. Bowman; Franklin E. White
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

The Data Fusion Model maintained by the Joint Directors of Laboratories (JDL) Data Fusion Group is the most widely-used method for categorizing data fusion-related functions. This paper discusses the current effort to revise the 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, 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 method for specifying data fusion processing and control functions, interfaces, and associate databases. The lack of common engineering standards for data fusion systems has been a major impediment to integration and re-use of available technology: 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.

Paper Details

Date Published: 12 March 1999
PDF: 12 pages
Proc. SPIE 3719, Sensor Fusion: Architectures, Algorithms, and Applications III, (12 March 1999); doi: 10.1117/12.341367
Show Author Affiliations
Alan N. Steinberg, ERIM International, Inc. (United States)
Christopher L. Bowman, Data Fusion and Neural Networks (United States)
Franklin E. White, Space and Naval Warefare Systems Ctr., San Diego (United States)


Published in SPIE Proceedings Vol. 3719:
Sensor Fusion: Architectures, Algorithms, and Applications III
Belur V. Dasarathy, Editor(s)

© SPIE. Terms of Use
Back to Top