Share Email Print

Proceedings Paper

Random-set approach to data fusion
Author(s): Ronald P. S. Mahler
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

This paper describes a fundamentally new theoretical approach to data fusion based on a novel type of random variable called the random finite set, and on a generalization of the familiar radon-nikodym derivative from the theory of the Lebesgue integral. We have shown how to directly generalize classical (i.e., single-sensor, single-target) parametric point estimation theory to the multi-sensor, multi-target, localization and classification realm. Using this theory we have shown that it is possible to construct data fusion algorithms in which detection, correlation, tracking and classification are unified into a single probabilistic procedure. We have also shown that a Cramer-Rao inequality holds for a general class of data fusion algorithms, apparently the first ever.

Paper Details

Date Published: 29 July 1994
PDF: 9 pages
Proc. SPIE 2234, Automatic Object Recognition IV, (29 July 1994); doi: 10.1117/12.181026
Show Author Affiliations
Ronald P. S. Mahler, Unisys Corp. (United States)

Published in SPIE Proceedings Vol. 2234:
Automatic Object Recognition IV
Firooz A. Sadjadi, Editor(s)

© SPIE. Terms of Use
Back to Top