Adaptive fusion of correlated local decisions

Document Type

Article

Publication Date

12-1-1998

Abstract

In this paper, an adaptive fusion algorithm is proposed for an environment where the observations and local decisions are dependent from one sensor to another. An optimal decision rule, based on the maximum posterior probability (MAP) detection criterion for such an environment, is derived and compared to the adaptive approach. In the algorithm, the log-likelihood ratio function can be expressed as a linear combination of ratios of conditional probabilities and local decisions. The estimations of the conditional probabilities are adapted by reinforcement learning. The error probability at steady state is analyzed theoretically and, in some cases, found to be equal to the error probability obtained by the optimal fusion rule. The effect of the number of sensors and correlation coefficients on error probability in Gaussian noise is also investigated. Simulation results that conform to the theoretical analysis are presented at the end of the paper. © 1998 IEEE.

Identifier

0032074937 (Scopus)

Publication Title

IEEE Transactions on Systems Man and Cybernetics Part C Applications and Reviews

External Full Text Location

https://doi.org/10.1109/5326.669570

ISSN

10946977

First Page

276

Last Page

281

Issue

2

Volume

28

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