By Topic

Fusion of detection probabilities and comparison of multisensor systems

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Krzysztofowicz, R. ; Dept. of Syst. Eng., Virginia Univ., Charlottesville, VA, USA ; Long, D.

A Bayesian detection model is formulated for a distributed system of sensors, wherein each sensor provides the central processor with a detection probability rather than an observation vector or a detection decision. Sufficiency relations are developed for comparing alternative sensor systems in terms of their likelihood functions. The sufficiency relations, characteristic Bayes risks, and receiver operating characteristics provide equivalent criteria for establishing a dominance order of sensor systems. Parametric likelihood functions drawn from the beta family of densities are presented, and analytic solutions for the decision model and dominance conditions are derived. The theory is illustrated with numerical examples highlighting the behavior of the model and benefits of fusing the detection probabilities

Published in:

Systems, Man and Cybernetics, IEEE Transactions on  (Volume:20 ,  Issue: 3 )