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An Estimator for Multi-Sensor Data Fusion

Authors: Chandrashekhara Thejaswi P. S.; S. Viswanath Ganapathy; Ranjeet Kumar Patro; Manik Raina; Subhas Kumar Ghosh;

An Estimator for Multi-Sensor Data Fusion

Abstract

In this paper, we examine binary hypothesis testing and parameter estimation problem in a sensor network. We address the problem of detection and also the estimation of the underlying parameter at the fusion center by optimally combining the test statistics sent by different sensors. We make no assumptions on the noise statistics at the sensor nodes except that their first and second order statistics are known. We show that the proposed method is optimal as an estimator as well as a detector.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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