Optimal Data Fusion of Correlated Local Decisions in Multiple Sensor Detection Systems

Moshe Kam; Qiang Zhu; Steven Gray

doi:10.1109/7.256317

Optimal Data Fusion of Correlated Local Decisions in Multiple Sensor Detection Systems

Moshe Kam, Qiang Zhu, Steven Gray

Research output: Contribution to journal › Article › peer-review

163 Scopus citations

Abstract

Recently, Chair and Varshney have solved the data fusion problem for fixed binary local detectors with statistically independent decisions. We generalize their solution by using the Bahadur-Lazarsfeld expansion of probability density functions. The optimal data fusion rule is developed for correlated local binary decisions, in terns of the conditional correlation coefficients of all orders. We show that when all these coefficients are zero, the rule coincides with the original Chair-Varshney design.

Original language	English (US)
Pages (from-to)	916-920
Number of pages	5
Journal	IEEE Transactions on Aerospace and Electronic Systems
Volume	28
Issue number	3
DOIs	https://doi.org/10.1109/7.256317
State	Published - Jul 1992
Externally published	Yes

All Science Journal Classification (ASJC) codes

Aerospace Engineering
Electrical and Electronic Engineering

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10.1109/7.256317

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abstract = "Recently, Chair and Varshney have solved the data fusion problem for fixed binary local detectors with statistically independent decisions. We generalize their solution by using the Bahadur-Lazarsfeld expansion of probability density functions. The optimal data fusion rule is developed for correlated local binary decisions, in terns of the conditional correlation coefficients of all orders. We show that when all these coefficients are zero, the rule coincides with the original Chair-Varshney design.",

author = "Moshe Kam and Qiang Zhu and Steven Gray",

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Optimal Data Fusion of Correlated Local Decisions in Multiple Sensor Detection Systems

Abstract

All Science Journal Classification (ASJC) codes

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