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Proceedings Paper

Defining a fusion-gain system operation characteristic (SOC) curve based on probability of detection and probability of false alarms
Author(s): Erik P. Blasch; Jim Hoffman; Joe Petty
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Paper Abstract

One issue that concerns the data fusion community is whether or not fusion of sensory information is beneficial. Beneficial results from fusion can be understood from a logical argument that if two types of sensors are measuring an object and only one source is available, then fusion is beneficial. Such an argument holds in the case of person identification system with the fusion of audio and video information. If only video is available, then a system comprising of audio alone could not identify the person. We further refine the fusion benefit to assess what is the measure of fusion gain? A fusion gain system operator characteristic (FG-SOC) metric and a system reliability (SR) metric are used to define a fusion gain. A multi-source data fusion system performance modeling gain directly addresses both system performance and data sufficiency using system simulation and functional modeling methods. The FG- SOC approach models the relationships between sensor performance, revisit rate, and object density by extending current statistical tracking performance models to asynchronous sensing situations. The FG-SCO application establishes a method for the relative comparison of multiple sensor collection alternatives using a functional performance characterization and can be used to evaluate sensor fusion planning and control alternatives based on fusion system performance.

Paper Details

Date Published: 16 August 2001
PDF: 9 pages
Proc. SPIE 4380, Signal Processing, Sensor Fusion, and Target Recognition X, (16 August 2001); doi: 10.1117/12.436966
Show Author Affiliations
Erik P. Blasch, Air Force Research Lab. (United States)
Jim Hoffman, Lockheed Martin Astronautics Operations (United States)
Joe Petty, Lockheed Martin Astronautics Operations (United States)

Published in SPIE Proceedings Vol. 4380:
Signal Processing, Sensor Fusion, and Target Recognition X
Ivan Kadar, Editor(s)

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