Adaptive decision fusion using genetic algorithm

Document Type

Conference Proceeding

Publication Date

4-26-2016

Abstract

We consider a parallel distributed decision fusion system consisting of a bank of local sensors and a fusion center. Each local sensor makes binary decisions based on its own observations. The decision is to accept one of the hypotheses, H0 or H1. Each local sensor transmits its decisions to the fusion center over an error free channel. The fusion center combines all the local decisions to obtain a global decision. For observations that are statistically independent conditioned on the hypothesis and fixed local decisions, the Chair-Varshney fusion rule minimizes the global Bayesian risk. However, this fusion rule requires knowledge of local sensor performance parameters and the prior probabilities of the hypothesis set. In most applications, these are unavailable. Moreover, local sensor performance may be time varying. Several studies attempted on-line estimation of the unknown local performance metrics and prior probabilities. We develop a fusion rule that applies a genetic algorithm to fuse the local sensors' binary decisions. The rule adapts to time varying local sensor error characteristics and provides near-optimal performance at the expense of a larger number of observations and higher computational overhead.

Identifier

84992371746 (Scopus)

ISBN

[9781467394574]

Publication Title

2016 50th Annual Conference on Information Systems and Sciences Ciss 2016

External Full Text Location

https://doi.org/10.1109/CISS.2016.7460536

First Page

401

Last Page

406

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