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Data fusion without knowledge of the ground truth using Tseltin-like Automata

Yazidi, Anis; Sandnes, Frode Eika
Chapter, Peer reviewed, Chapter
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URI
https://hdl.handle.net/10642/3885
Date
2016
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  • TKD - Institutt for informasjonsteknologi [865]
Original version
Yazidi A, Sandnes FE: Data fusion without knowledge of the ground truth using Tseltin-like Automata. In: Rudas IJ. 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2016. IEEE p. 4501-4506  
Abstract
The fusioning of data from unreliable sensors has

received much research attention. The main stream of research

assesses the reliability of a sensor by comparing its readings to

the ground truth in an online or offline manner. For instance,

the Weighted Majority Algorithm is a representative example

of a large class of similar legacy algorithms. Recently, some

advances have been achieved in identifying unreliable sensors

without any knowledge of the ground truth

which seems a paradox

in itself. In this paper, we present a simple mechanism for solving

the problem using Tsetlin-like Learning Automata (LA). Our

approach leverages a Random Walk (RW) inspired by Tsetlin

LA so that to gradually learn the identity of the reliable and

unreliable sensors. In this perspective, we resort to a team of

RWs, where a distinct RW is associated with each sensor. By

virtue of the limited memory requirement of our devised LA,

we achieve adaptive behavior at the cost of negligible loss in the

accuracy.
Publisher
IEEE

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