Game-Theoretic Learning for Sensor Reliability Evaluation Without Knowledge of the Ground Truth

Abstract

Sensor fusion has attracted a lot of research attention during the few last years. Recently, a new research direction has emerged dealing with sensor fusion without knowledge of the ground truth. In this article, 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 results 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. The experimental results show the accuracy of our solution and its ability to deal with settings that are unsolvable by legacy works.