Integration of multiple adaptive algorithms for parallel decision fusion

Document Type

Conference Proceeding

Publication Date

4-26-2016

Abstract

The Chair-Varshney rule for parallel binary decision fusion requires knowledge of the a priori probabilities of the hypotheses and the performance of the sensors (probabilities of false alarm and missed detection). In most applications, this information is not available. Five methods were developed so far for estimating the unknown probabilities. However, none of them is the best under all circumstances. We present an algorithm that selects the best of these five methods. The algorithm estimates roughly the value of the a priori probabilities and the sensor performance from input data, and seeks support from a data base that provides archival data from the five methods at this operating point. In simulation, the algorithm performed on average better than each one of the five existing methods operating alone.

Identifier

84992347120 (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.2016.7460528

First Page

355

Last Page

359

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