By Topic

Sequential testing of multiple hypotheses in distributed 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)
Tartakovsky, A. ; Center for Appl. Math. Sci., Univ. of Southern California, Los Angeles, CA, USA ; Rong Li, X.

It is supposed that there is a multisensor system in which each sensor tests a finite number of hypotheses in a sequential manner. Then the decisions are transmitted to a fusion center, which combines them to improve the performance of the system. First we propose a local multihypothesis sequential test procedure which allows one to fix the probabilities of errors at specified levels and is asymptotically optimal for general statistical models in the sense of minimizing the expected sample size when the probabilities of errors vanish. We then construct two fusion rules-non-sequential and sequential. The first fusion rule waits until all the local decisions in all sensors are made and then fuses them. It is optimal in the sense of minimizing the average probability of error (Bayes criterion) or the maximal probability of error (minimax criterion). In contrast, the sequential fusion rule fuses local decisions one by one in the order they are made, and at each stage decides whether to continue fusion or to stop and make a final decision. It has an advantage over the first rule in that it reduces the total time to make a final decision, for a given average probability of error. An example of fusion of binary local decisions shows that the final decision can be made substantially more reliable even for a small number of sensors (3-5).

Published in:

Information Fusion, 2000. FUSION 2000. Proceedings of the Third International Conference on  (Volume:2 )

Date of Conference:

10-13 July 2000