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Proceedings Paper

Measures of information for multilevel data fusion
Author(s): Martin G. Oxenham; Douglas J. Kewley; Mark J. Nelson
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Paper Abstract

In many commercial and military activities such as manufacturing, robotics, surveillance, target tracking and military command and control, information may be gathered by a variety of sources. The types of sources which may be used cover a broad spectrum and the data collected may be either numerical or linguistic in nature. Data fusion is the process in which data from multiple sources are combined to provide enhanced information quality and availability over that which is available from any individual source. The question is how to assess these enhancements. Using the U.S. JDL Model, the process of data fusion can be divided into several distinct levels. The first three levels are object refinement, situation refinement and threat refinement. Finally, at the fourth level (process refinement) the performance of the system is monitored to enable product improvement and sensor suite management. This monitoring includes the use of measures of information from the realm of generalized information theory to assess the improvements or degradation due to the fusion processing. The premise is that decreased uncertainty equates to increased information. At each level, the uncertainty may be represented in different ways. In this paper we give an overview of the existing measures of uncertainty and information, and propose some new measures for the various levels of the data fusion process.

Paper Details

Date Published: 14 June 1996
PDF: 12 pages
Proc. SPIE 2755, Signal Processing, Sensor Fusion, and Target Recognition V, (14 June 1996); doi: 10.1117/12.243169
Show Author Affiliations
Martin G. Oxenham, Defence Science and Technology Organisation (Australia)
Douglas J. Kewley, Defence Science and Technology Organisation (Australia)
Mark J. Nelson, Defence Science and Technology Organisation (Australia)


Published in SPIE Proceedings Vol. 2755:
Signal Processing, Sensor Fusion, and Target Recognition V
Ivan Kadar; Vibeke Libby, Editor(s)

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