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


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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.




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