@misc{10481/72499,
year = {2020},
url = {http://hdl.handle.net/10481/72499},
abstract = {Sensor fusion has attracted a lot of research attention during the last years. Recently, a new research direction has emerged dealing with sensor fusion without knowledge of the ground truth. In this paper, we present a novel solution to the latter pertinent problem. In contrast to the first reported solutions to this problem, we present a solution that does not involve any assumption on the group average reliability which makes our 1·esults more general than previous works. We devise a strategic game where we show that a perfect partitioning of the sensors into reliable and unreliable groups corresponds to a Nash equilibrium of the game. Furthermore, we give sound theoretical results that prove that those equilibria are indeed the unique Nash equilibria of the game. We then propose a solution involving a team of Learning Automata (LA) to unveil the identity of each sensor, whether it is reliable or unreliable, using game-theoretic learning. Experimental results show the accuracy of our solution and its ability to deal with settings that are unsolvable by legacy works.},
organization = {European Commission	TIN2016-75850-P},
organization = {RUDN University Program	5-100},
publisher = {IEEE},
keywords = {Unreliable Sensors Identification},
keywords = {Game theory},
keywords = {Learning automata},
keywords = {Sensor fusion},
title = {Game-Theoretic Learning for Sensor Reliability Evaluation without Knowledge of the Ground Truth},
author = {Yazidi, Anis and Herrera Viedma, Enrique},
}